No no don’t go away, there’s actually some science in this post, courtesy of the increasingly-heavyweight Nick Stokes. Or, perhaps more fairly, whatever science there is comes from NS. But there’s a lot of snark too, as I hope you’d expect. That comes from me. The title isn’t quite right; I could have tried anopsologists but I bet you wouldn’t have recognised that – I wouldn’t have, until I looked it up. But let me attempt to come to the point. Over at JoNova is yet another of those tedious posts where they complain that a weather station has been “adjusted” to show warming (and they’re still gnawing on the same dry bone), and that one should only ever eat raw data because only raw data is good for you. If you find raw “skepticism” too raw, you could try the WUWT echo chamber which, errm, just echoes JoNova.

In this case the station in question is Amberly, and if you care about what’s actually gone on then you should of course read Nick Stokes and if that isn’t enough you could read part 2. Nick also has a convenient post showing the affects of adjustment on trends, which is reasonably convenient for pushing at people who claim (without bothering to check, obviously) that adjustments always push the trends upwards. Or this one.

JoNova has a “home crowd” who can be relied on to say the right things, much like some of the WUWT regulars. Here’s one saying the artificial concept of homogenisation (which literally means removing all difference), is an act of unthinking vandalism which is probably typical enough; I don’t think I’ll bother quote any more. What’s unfortunately-not-at-all-surprising in this case is that they’re all mouthing off without having bothered to do even the most basic of checks. Is there good metadata for this station, to rule out a move or other change that might justify an adjustment? No. Is that the Bureau Of Meteorology’s fault? Not really; the station was run by the RAAF when the “move” occurred. That doesn’t stop some of the commentators there actually literally accusing the BoM of criminal behaviour.

Relative to JoNova, the WUWT echo chamber is a friendly place where scholars have a reasoned scientific debate. (Never thought I would be able to write that.)

Maybe for the people without orthorexia nervosa, you should mention that the fruitarian diet is a subgroup of the raw food diet. (A cheap pun on people calling everyone that does not to eat the standard lovely processed food diet, a orthorexia patient.)

If 80% of the stations had the same trend bias and only 20% the right trend, homogenization would make the trend worse. Even the scientists that are not game designers know this. If these stations have a constant bias there is no problem. If they have sudden jumps in temperature due to non-climatic reasons homogenization can remove part of the trend bias. Thus your formulation “if the majority of sample has a systematic bias, homogenization simply does not work … (Even Dr. Venema goes this far.)”, is not a fair representation of my position; at least you should talk about a trend bias.

Your explanation that the micro-siting exaggerates the global temperature trend is a statistical description of what you have found. It is not an explanation of what exactly goes wrong at the location of the measurement, why would a thermometer in a location with bad micro-siting have a temperature bias that increases a little bit every year in the 1990s and why would this bias stay constant in the 2000s? That is what I call a “physical mechanism” in the above linked discussion.

In case of the UHI we can understand why the bias becomes larger and larger over time, because the city keep on growing. In the UHI case there is no “exaggeration”, the local non-climatic trend bias is independent of the global mean temperature. You propose something a lot more complicated to explain. In case of micro-siting I cannot see an explanation similar to the growth of a city and certainly not something that would “exaggerate” and thus depend on a global temperature trend. I had hoped that since our discussions in April this year, you had given such an explanation some thoughts.

Without a physical mechanism, all you have is a statistical result. That could be a small paper. That would be publishable in the hope that someone else later comes up with a physical mechanism. With a mechanism you would have something really interesting. Only when we know the mechanism can we judge whether this non-climatic change is a trend bias or jumps.

Hello, Victor, it is always a pleasure to have your input. I know you disagree with me on this issue, but that makes it all the more valuable. And thanks to Dr. Connolley for directing me here. I will take this in bite-sized pieces so as not to overwhelm.

I think you have partly misunderstood what I have said. Please allow me to explicate.

If 80% of the stations had the same trend bias and only 20% the right trend, homogenization would make the trend worse.

Yes, the bias is in trend. Not offset. TREND. Sorry I was not entirely clear about that. Of course I am talking about trend. Nothing else would make any sense at all. Heck, I anomalize the data. Otherwise, how can I compare stations in Florida with stations in Minnesota? One missing month or year from either would create a spurious trend change if the data were not anomalized. I don’t even know what the offsets are. I deal strictly and only in trend.

is not a fair representation of my position; at least you should talk about a trend bias.

Yes, of course. I am talking about trend bias. I took that to be obvious. Sorry I was not sufficiently specific. I therefore believe I have characterized your position accurately, and somewhat cautiously at that.

I am well aware that a step change can screw up a longterm trend, but I am not talking about that. I am talking about a constant divergence in slope. Not a spurious trend created by a TOBS (etc.) step change.

So I think we can agree that far?

Your explanation that the micro-siting exaggerates the global temperature trend is a statistical description of what you have found.

Indeed it is. But read on.

It is not an explanation of what exactly goes wrong at the location of the measurement, why would a thermometer in a location with bad micro-siting have a temperature bias that increases a little bit every year in the 1990s and why would this bias stay constant in the 2000s? That is what I call a “physical mechanism” in the above linked discussion.

I can answer this clearly and completely:

it is because heat sink effect does not create a trend. It exaggerates an already-existing trend. Let us examine the history.

This trend is exaggerated by poor (but constant, unchanging) microsite, and there is divergence.

Over the last decade, however there is no atmospheric trend (anthropogenic CO2 forcing counteracted natural negative PDO).

For this interval, there is no trend to exaggerate. Therefore there will be no divergence.

If there was a divergence, during the last decade, that would dispute our hypothesis. The lack of divergence that you point out strongly supports our hypothesis.

For that matter, I carefully selected the longest, sharpest cooling trend for for which the vital metadata exists, i.e., the 1998 peak as start-point and the 2008 end-point.

As expected, the poorly sited stations cooled faster than the well-sited stations.

Bad microsite exaggerates trend. If it is a warming trend, it will exaggerate the warming,. If there is a cooling trend, it will exaggerate the cooling. And if there is no trend, there will be no exaggeration at all in either direction.

The hypothesis works during a warming trend. The hypothesis works during a cooling trend. And the hypothesis works during a flat trend. Hard to beat that. It all fits.

In case of the UHI we can understand why the bias becomes larger and larger over time, because the city keep on growing. In the UHI case there is no “exaggeration”, the local non-climatic trend bias is independent of the global mean temperature.

I agree entirely on all points.

You propose something a lot more complicated to explain. In case of micro-siting I cannot see an explanation similar to the growth of a city and certainly not something that would “exaggerate” and thus depend on a global temperature trend.

Indeed I do. And you are quite correct in that the explanation is entirely different. In the case of urban growth, there is a change of environment that impacts the station reading. But in the case of our study, I am talking about environments that are constant and unchanging throughout the study period.

If the microsite rating is different at the start of our study period than it was at the end of our study period, we drop the station even if it hasn’t moved. And we drop moved stations (this sadly reduces our sample size, but it is obviously necessary).

This is an entirely different issue than UHI. It strikes directly to the heart of Menne et al., 2009 & 2010.

I had hoped that since our discussions in April this year, you had given such an explanation some thoughts.

I have indeed given that very concept much thought for the last half-decade. My original concept was similar to your UHI explanation. But I now find that the diversion occurs even if the environment of the station remains unchanged throughout the study, which directly refutes Menne et al.

Thanks Evan. I admit to being confused by your statement “This trend is exaggerated by poor (but constant, unchanging) microsite, and there is divergence.”

First, I assume that “microsite” equates to something like “microclimate of the site.” Please correct me if that’s wrong.

Second, I don’t see how a heat sink can exaggerate a trend, rather than reducing the trend. I’ll give my understanding of the situation here:

The argument seems to be that the observed temperature is reduced by a “heat sink” effect. Then if the real temperature is T, the observed temperature Tobs will be reduced by the magnitude dT of the heat sink:

Tobs = T – dT

If dT is constant then there obviously is no change to the trend. So let’s express the heat sink dT in a simple way as

dT = T – Ts

where Ts is an imaginary cold temperature (a big block of dry ice, or whatever). As long as T > Ts, we have a heat sink dT.

Now let’s suppose that we have a warming trend. The correct temperature T increases. Then the heat sink dT=T-Ts also increases, since Ts is constant. This means we subtract more from T to get the observed temperature Tobs = T – dT. Thus the observed trend is damped, rather than exaggerated.

I think this is the sort of thing Victor might mean by a “physical mechanism.” The preceding is obviously simplistic, and I may well have misunderstood what you mean by a heat sink. But I propose that approaches like this give us a starting point for discussion of physical mechanisms.

Without a physical mechanism, all you have is a statistical result. That could be a small paper. That would be publishable in the hope that someone else later comes up with a physical mechanism.

Again, I agree.

With a mechanism you would have something really interesting. Only when we know the mechanism can we judge whether this non-climatic change is a trend bias or jumps.

It is tend bias, not jumps.

You can see the jumps in the stations that are affected by TOBS bias. We drop all stations that have an evening-to-morning TOBS flip (and vice-versa).

And we apply a warming adjustment for MMTS conversion, using Menne et al. as our basis. It depends, of course, at what point conversion occurred. If it is near the start or end points of the study period, the effect on trend is smaller. It it occurs near the middle, it is, of course, much larger.

According to Menne, the average effect on MMTS stations is ~0.02 per decade to Tmean (not dramatic, but not chump change, either). The aggregate change for all stations, including those which did not convert is ~0.014.

We accept this finding at this time, and (on my insistence) apply a consistent adjustment.

We now use raw+MMTS coversion data as our basis, not raw data, as we did in 2012. We wish we could avoid this, but we cannot, as ~three quarters of stations converted to MMTS during our study period.

Almost any longer time series contains break inhomogeneities (jumps); On average there is one jump every about 20 years. Also datasets where all time series contain non-climatic changes can be successfully homogenized to achieve more accurate estimates of the real climatic trend.

I know, I know. Those step changes are primarily due to TOBS flip. That is precisely why we drop all such stations. And it is why we adjust for MMTS conversion.

Station moves, not so much, however. I have found that when I dropped all moved stations from our dataset, it made very little difference. Possibly this is because a move can have either a warming or cooling effect, while TOBS change in the US, on the other hand, is almost completely an evening to morning shift (resulting in a spurious cooling effect on trend.

But whatever the reason, moves have little effect on our results. In fact, dropping moved stations slightly strengthens our results (as might be expected, as this reduces noise).

Our current, pristine, dataset is almost entirely free of step changes. That allows us to isolate microsite as an area of study using near-raw data, adjusted only for MMTS conversion.

Relative to JoNova, the WUWT echo chamber is a friendly place where scholars have a reasoned scientific debate. (Never thought I would be able to write that.)

I speak well of you and Dr. Connolley there whenever the opportunity arises. It would please me very much if I could somehow effect a reconciliation between you and Anthony. Light:Heat ratio not currently working out to my satisfaction.

You are valuable assets. I dislike waste. My mind needs to push back, not go with the flow. For that I need something to push back against. Besides, I like you both for the civilized manner in which you have treated me. Hands across the water. Hands across the sky.

A heat sink, in the context of mini-max temperature measurement works as follows:

Heat is absorbed by the nearby sink. When the temperature goes down, so does the heat sink, but at a slower rate than the surrounding atmospheric temperature absent the heat sink.

When Tmin rolls around, the heat sink has not yet completed the process of exuding its daily absorbed energy, and is higher than the “nautral state of the atmosphere” surrounding it. A function of that exuded heat affects the sensor reading.

That is the premiss underlying Leroy (2010). But Leroy is concerned only with offset, not trend. And that is where we take Leroy’s conditions and take it a step further, applying it to trend (sic).

It appears (from the data) that the difference changes over time even if the microsite conditions of the sensor remain unchanged.

The difference is greater at Tmin than Tmax because absorbed heat of the sink is still accumulating at Tmax (but is still warmer than the sensor), while at Tmin, the heat is still exuding (and is proportionally greater compared with the sensor at Tmax).

The trend bias occurs because the delta between the heat sink and the sensor at the start of a warming trend is greater at higher temperatures, and therefore the trend is increased at the end than at the onset. (Vice-versa for cooling trend, and no trend bias at all if the trend is flat.)

That is where we butt heads with Menne (2009). I have been informed that I have a very hard head. So be it.

Menne’s hypothesis is that if a sensor’s microsite conditions are unchanged throughout the study period, the amount of exuded heat affecting the sensor remains unchanged throughout, and therefore does not bias the trend. We demonstrate otherwise.

Menne simply moved on, ignoring Leroy (2010), which I feel he shouldn’t have. (He has his confirmation bias, I have mine.) Wed in haste, repented at leisure. I predict (project?) that the boyz at NOAA will find themselves with a lot of toothpaste to get back in the tube. And it won’t end there.

I think placing your thoughts immediately in the context of trends is confusing matters. As far as I can tell your argument is simply that, on a warm day, a badly-sited station will tend to be more anomalously warm than a well-sited station. And vice-versa for cold days.

Of course, if this were true and the number of warm days were to increase (and the number of cold days decreased), the recorded trend would be greater than the true trend.

The only reason bad microsite has created a warming bias (study period 1979 – 2008) is that there was genuine warming. During that period. During the current flat phase one would not expect trend exaggeration in either direction.

Poor microsite will tend to exaggerate tend in either direction. Leroy also claims that the absolute offset is higher for poorly sited stations. He does not address trend at all.

The premise of Menne (2009) is that even though the offsets are higher for a poorly sited station, the trend will remain the same. He claims only a change in the microsite itself will affect trend (by creating a jump shift). So if a station is Class 4 in 1979 and is Class 4 in 2008, the trend will not be affected, while if a Class 2 becomes a Class 4 over that interval, it will affect trend.

We find this to be incorrect. We find that even if a station is Class 4 throughout, the trend will be exaggerated, not merely the offset. The only reason microsite is a warming bias to tend is that there is genuine global warming.

When one is conducting such a study, one has to do one’s best to drop stations with TOBS-bias, which will create a jump shift.

We remove all moved stations except in a small handful of cases where the move is localized, both locations are known, and the microsite rating is the same in both locations. In nearly all cases where there is a move, the prior location is unknown, so we drop the station. The rating probably will not have changed, but we do not know, so we must drop, even if there is no step change in the data.

Fortunately we still have 400 stations remaining, so our sample size is large enough. The USHCN may have its problems, but at least they heavily oversamples. This allows us to strip out the pristine, well sited stations and still have sufficient numbers and distribution.

Also, what’s sauce for the USHGN is sauce for GHCN. he microsite problem appears to be global. I have spot-checked a few of the GHCN stations, and the problem seems to be as bad, but I have not done a comprehensive survey, so I cannot be certain.

Perhaps when we publish, there will be an opportunity to put in for some grant money (Greenpeace, Koch brothers, I don’t give half a damn) and take the show on the road.

I can’t survey GHCN using current resources for a number of reasons.

— The coordinates provided are to only to two decimal places (utterly insufficient; I need at least four).

— While I am currently the world’s leading expert on spotting and rating stations (a mini-scandal in and of itself), my expertise only includes spotting CRS, MMTS, and ASOS/AWOS setups. I cannot currently recognize some of the odder-looking equipment.

— Anthony’s mercury monkeys (of which I am pleased to include myself) have provided photographs of the great majority of USHCN stations, which makes them easy to pick out on Google Earth, drop my 5m, 10m, and 30m circles around the sensors. I have spotted quite a number without the photos, too, as Google earth has radically improved over the last 5 years. What used to be a blur is now a nice crisp image with a nice little dot and a distinctive shadow. I then draw polygons around the heat sinks and pop up their areas, permitting a careful evaluation.

— GE is currently is excellent for the US, but for Outer Mongolia, not so much. With insufficient coordinates, I can’t even locate a CRS box in most cases, especially if the GE image does not have sufficient resolution.

There is also the lamentable state of non-US metadata to contend with. I agree with Mosh that the answer is in the data. I disagree with Mosh, however, that one can reliably reverse-engineer the data to infer the metadata by looking for jumps and doing pairwise comparisons. Sometimes a jump is perfectly natural and an accurate representation. And the non-US stations tend to be too widely separated for pairwise (leading to some near-comical examples).

So a full GHCN survey must be done mano-a-mano, and I haven’t the resources, even if they would allow me near the stations (doubtful) and even if the metadata were sufficient (also doubtful).

Mesosite (rural v. urban and like that) won’t do. microsite is king. A badly sited rural station will, on average, warm faster than a well sited urban station. On top of that, urban stations tend to have better microsite than rural stations (Peter, Parkinson, 2006, confirmed by my own observations).

So we absolutely cannot say a station is well sited just because it is rural. In fact, I find that while UHI often has a walloping effect on offset, urban trends are not much different from rural. But that premise does not zoom in to microsite, which demonstrably makes a large difference to trend.

Menne figured that what works for mesosite works for microsite. That may be intuitive, but it is wrong.

Maybe I should apply for a job with NOAA. Maybe they would come to the (probably correct) conclusion that it would be worth a salary to them to have me inside the tent pissing out rather than outside the tent pissing in.

But there is bad blood between us, and I doubt that they would accede. I would be more than willing to st aside our differences, but I am not confident that the reverse is true. Also, my difference of approach (i.e., outright rejection of their methods of adjustment) would stand in the way.

[Your publication record would stand against you for anything but an entry-level job as being rather thin. I’d also hold against you your failure to protest the absurd hoopla AW put out 2+ years ago. I can see it would be awkward for you, but nonetheless it stands -W]

Evan Jones: “Yes, the bias is in trend. Not offset. TREND. Sorry I was not entirely clear about that. Of course I am talking about trend.”

I know it, you know it, I know you know it, but your reader does not know it and get’s the impression that the problem is much, much larger than it actually is.

Evan Jones: “I am talking about a constant divergence in slope. Not a spurious trend created by a TOBS (etc.) step change. So I think we can agree that far?”

Almost. :-) Your 80% of badly sited stations are not all equally badly sited. Thus one would expect that the divergence in the slope is also different within this 80%. That will still make it impossible to homogenize a dataset that would have so much slope inhomogeneities (non-climatic changes), but you may notice what a mess it is, when all stations have a different trend.

Evan Jones: “it is because heat sink effect does not create a trend. It exaggerates an already-existing trend.”

May I interpret the term heat sink as a substance that absorbs heat when the air is warmer and releases heat when the air is colder?

(In the classification of Leroy, stations can also be considered bad without having such a substance nearby, a simple nearby bush is sufficient to be classified as bad.)

If in that case (ignoring the daily and yearly cycle) the temperature would rise constantly (linearly), the heat sink would produce a short delay and after that the temperature trend would be the same again, the same as it would have been without the heat sink.

In case the temperature fluctuates strongly from year to year (again ignoring the daily and yearly cycle), the only influence of the heat sink would be to dampen the variability of the temperature.

I do not see how a heat sink would aggagerate a temperature trend on a decadal scale. (Even if it would do something on a time scale longer than a year, I would expect the influence to be limited to days, maybe weeks.) Thus I am still as confused as Raymond Arritt.

VV: “Only when we know the mechanism can we judge whether this non-climatic change is a trend bias or jumps.”

Evan Jones: “It is tend bias, not jumps.”

I do not think you can know this as long as you refuse to detect jumps by comparing your stations to its neighbors. You cannot assume that you have station history information on every possible jump that happened to these stations.

In fact, that is where my main suspicion is. You did not find much in the last decade because there your station history is still quite good. Whereas you do find biases in the last century because then your information on the station history becomes worse and worse. The observers you talked to this year will often not have been the observers in the 20th century and know less about what happened then. And even if it is the same person, memory fades.

Whatever the mechanism will turn out to be, I expect that it will be something that generates jumps. This problem is about micro-siting. Thus by definition the changes are close to the thermometer. In case of the Urban Heat Island (UHI) we speak about many changes in a large area, that smoothes out to a gradual change. In case of micro-siting problem near the instruments, the number of changes will most likely by limited and I would expect to get jumps. (They might be too small to detect, especially if the neighboring stations are far, but I would really like to encourgage you to try to detect jumps. That would make your study a lot stronger. I see no reason why, but if Anthony Watts has emotional problems with correcting these jumps, you do not have to, you could just remove the stations where you detected jumps. You can get free software at http://www.homogenisation.org.)

Evan Jones: “S = Amount of change to sensor measurement caused by heat released from sink”

Good to see some equations, they can help to make your statements more precise. But it still sounds like a statistical description of what you found, rather than a physical mechanism of what happens to the thermometer.

In the language of these equations, my question would be why ΔS is increasing, what made your heat sind release some and more heat, to keep on increasing ΔS? And it is still releasing heat to keep ΔS more of less the same the last decade (the small decrease the last decade is smaller than the inncrease we had in the last century). That is an aweful lot of energy. Where does that come from?

I think the suggestion of Paul S is very good. Try to formulate your hypothesis in terms of temperature (or energy), that is what is measured. The effect on the trend should be a consequence that follows from your assumptions on the physics of temperature or energy.

Evan Jones: “I speak well of you and Dr. Connolley there whenever the opportunity arises.”

That is much appreciated, especially as that does not make you more popular with your boss and friends.

Evan Jones: ” I like you both for the civilized manner in which you have treated me.”

You put in quite some work and you are polite yourself, then I am willing to provide feedback. As far as I can. (Still have to reply to the Connolly family, who also made quite an effort and thus also deserve a good reply.)

You typically have to build up a solid reputation over several years and studies before you apply for project funding, one article won’t do, at least not with the NSF or DOE. Even good proposals with big names behind them have nowadays not more than 10, 20% chance in the US. However, if you write a convincing article, scientists will want to continue the work. And maybe you can find an experienced scientist that would like to write a research proposal together.

I know it, you know it, I know you know it, but your reader does not know it and gets the impression that the problem is much, much larger than it actually is.

But surely the very fact that it is trend makes the problem so serious. And so subtle. And so evasive. And so diabolical. And so easy to miss using existing tools. You must know where to look.

(In the classification of Leroy, stations can also be considered bad without having such a substance nearby, a simple nearby bush is sufficient to be classified as bad.)

Well, yeah. But we only use his ratings for heat sink. We do not rate for shade or slope or vegetation. Heat sink only. The only other big player in this is shade. Thing is, shade is not an independent variable — more often than not, it’s the heat sink itself which is casting the shade.

So if I say a station’s siting is bad, I mean it’s BAD. Heat sink bad. Not some eeny-weeny bush.

The other variables are minor players, anyway. Perhaps a subject for a followup. We started this shindig before we even heard of Leroy. It was always about heat sink. We are not studying Leroy. We are studying heat sink. Leroy is only our tool to do that. it’s a Swiss-Army knife, sure, but all we need is the big blade. Which we wield with deadly effect. But we are not signing up for the Swiss Army. Later, maybe.

Almost. :-) Your 80% of badly sited stations are not all equally badly sited. Thus one would expect that the divergence in the slope is also different within this 80%

There is not a lot of difference between Class 3\4\5, actually. Only ~0.02C per decade.

I do not think you can know this as long as you refuse to detect jumps by comparing your stations to its neighbors.

I see very similar wiggles in the graphs of 3\4\5 vs. 1\2. The divergence is not a result of step change.

It’s even true on a regional level 99 of them), where the numbers of stations are much smaller than the overall (and that is much the equivalent of pairwise comparison, just cut more crude).

You cannot assume that you have station history information on every possible jump that happened to these stations.

That’s why god made oversampling.

And likewise, you cannot assume that an jump is an error merely because it is a jump. That’s where I depart from you and Mosh. Climate shifts around. Sometimes it just gets warmer or colder in that neck of the woods — and even stays that way.

And okay, I will attempt to put a stop to the inappropriate puns. More than fair enough. In order to end a war, someone has to stop shooting first. I don’t even care who it is, just so long as the war ends.

Anthony is genuinely ambivalent about you. He knows you have encouraged rapprochement. But the shots keep going on, and it grieves me. You have to understand that I am Anthony’s footsoldier. I have enormous respect for him, and ironclad loyalty. Nothing is going to change that.

As for grants, I quite agree with you. But I don’t do “typical”.
To heck with DoE. They would undoubtedly regard me as a deadly foe, anyway. If anything, they would pay $$$ to falsify my work. I would probably go the private sector route. Then I only have to convince one person, not a bureaucracy. I have spent maybe 3000 hours on this project. And continuing. I daresay that’s a tad more than goes into a typical peer-reviewed paper.

May I interpret the term heat sink as a substance that absorbs heat when the air is warmer and releases heat when the air is colder?

Yes.

Tmax is a slightly different story, but only slightly.

Good to see some equations, they can help to make your statements more precise. But it still sounds like a statistical description of what you found, rather than a physical mechanism of what happens to the thermometer.

Ah. You seek the elusive fS. That is beyond my pay-grade and modest intelligence level. Yes, we will have it, but it will come from someone other than myself. What I have done is state the hypothesis and provide the symbolic framework.

The general idea is that the delta between S and T is higher with higher temperatures and it takes the heat longer to bleed off. So it affects the sensor more at higher temps than lower. And temps are higher at the end of a warming trend than before. That’s the logic of it.

And vice-versa and like that.

That’s the English description of fS. We shall rent us a sure ’nuff physic to patch in the equation.

Evan, can you give us an example of a specific situation where the heat sink would exaggerate both warming and cooling trends? A concrete example would help to put the equations and assumptions into context.

Surely. I will give an example of all three conditions. It will be a USHCN example, because those are my little MMTS brothers and CRS sisters and I love them so.

First, let us look at a warming trend. 1979 -1998 will do. The poorly sited stations warm much faster than well sited stations.

Then let us look at the 1998 – 2008 data. That is a carefully selected interval because it maximizes the cooling (high start point and low end point). The poorly sited stations cool faster than the well sited stations over that interval.

Now let us look at the last 10 years. USHCN data compares very well with CRN (stations so beautiful, well equipped, and well sited they would move a heart of stone to tears). No surprise here — the trend is flat.

Those are my three posterboard examples. (And I owe the last one to the VeeV, actually.) They demonstrate the the Delta Sink hypothesis works up, down — and sideways. It rings and sings.

Perhaps more for what I will have published than for what I have not published, I think. #B^)

But if you go to the USHCN adjustment page you will find my name there (as a co-author of Dr. Fall).

for anything but an entry-level job as being rather thin.

What makes you suppose I would turn down an entry-level job?

You got a fast car
I want a ticket to anywhere
Maybe we make a deal
Maybe together we can get somewhere
Any place is better
Starting from zero got nothing to lose
Maybe we’ll make something
Me myself I got nothing to prove
You got a fast car
I got a plan to get us out of here
. . .I, I had a feeling that I belonged
I, I had a feeling I could be someone, be someone, be someone

Actually, I have no qualifications — whatsoever — except in the one area they so desperately need (but stridently insist that they do not).

I’d also hold against you your failure to protest the absurd hoopla AW put out 2+ years ago. I can see it would be awkward for you, but nonetheless it stands -W]

Would you, now? I am #2 author of said hoopla. #B^)

[By hoopla I don’t mean the paper. I mean the absurd and utterly indefensible over-publicity for a mere draft paper -W]

I think you have been laboring under a misapprehension. Anthony made the 2012 pre-release for the expressed purpose of eliciting criticism prior to peer review. Those on your side of the debate scoffed it down as a publicity grab. It never seems to have occurred to you that he just might actually mean what he said.

Fortunately not all conservative Christians are like Watts and his ilk. Maybe it helped that I had just had a long detailed and mainly pleasant discussion with Evan Jones on the Watts et al. (2012) manuscript. If people want to honestly discuss science, I am happy to talk to them if I have some expertise. He writes:

That was good of you Victor.

It may surprise you a little to discover that I am a big-government, liberal atheist. And a great admirer of LBJ and the Great Society. (I have voted for both parties. And there is only one candidate I ever voted for twice.)

But what of it? A USHCN Class 2 MMTS runs hot or cold. It does not run right or left.

You may also have noticed that I avoid politics like the plague in these discussions. Politics has no place in science. (Not even political science, come to think of it.) Heck, I have gone to so much time and effort to unmuddy the data. Why on the green hills of earth would I want to muddy it with partisan politics?

But I am a liberal, born and raised. Forged on the anvil of New York City private schools. Trained in deconstruction and the dialectic. (And my erstwhile brethren are not entirely entertained when I turn their own weapons upon them.)

Evan – As WC points out, your continuing defense of the WUWT announcement 2 years ago only saps at your own credibility.

WUWT publishing suspended – major announcement comingSomething’s happened. From now until Sunday July 29th, around Noon PST, WUWT will be suspending publishing. At that time, there will be a major announcement that I’m sure will attract a broad global interest due to its controversial and unprecedented nature.

To give you an idea as to the magnitude of this event, I’m suspending my vacation plans. I weighed the issue, and decided (much to my dismay) this was more important. I can go on vacation trips another time, but this announcement is not something I can miss now and do later.

Media outlets be sure to check in to WUWT on Sunday around 12PM PST and check your emails.

I especially like: “This announcement is not something I can miss now and do later.” Yep, if he’d gone on vacation and made the announcement two weeks later who could even begin to unravel how changed a world we’d be living in???

The science will stand or fall on its own, but you should understand why many of us who have to hide a smirk. Well, that and he posts articles by any crackpot with a ‘theory’ – as long as it criticizes AGW :)

[Yes, that’s the stuff I’m referring to.

Aside (since wild rumour-mongering is fun): I speculated at the time, and I still think it likely: that announcement as actually meant to be something else; something that had to be pulled at the last minute. The paper pre-print was stuffed in at the last minute to cover the gap. That, too, is not to the discredit of the paper itself -W]

That is interesting information that may help us find an explanation for your result.

Had it been a heat sink that takes up or releases heat over the time scale of one decade, you would expect that it does matter how big the heat source is or how close this heat source is to the instrument.

So maybe we should look for something that happens at these kind of stations. Maybe the heat sink is not the reason, but the presence (yes/no, not the size) of a heat sink is correlated to some other problem. Just thinking out loud, how I would search for interesting ways to analyse the dataset.

Evan Jones: “That’s why god made oversampling. And likewise, you cannot assume that an jump is an error merely because it is a jump. That’s where I depart from you and Mosh. Climate shifts around. Sometimes it just gets warmer or colder in that neck of the woods — and even stays that way.”

Then let’s use that man-made oversampling to detect jumps that happen in individual stations. :-)

Yes, the assumption is not perfect. Especially when you have land-use changes, irrigation, clearing of woods, drying of swamps, and do on. Then there may be stuff happening regionally that we cannot distinguish from a local effect, if the station density is not sufficient.

However, the data with all the non-climatic jumps is worse. Especially when it comes to the large-scale (global) climate signal.

Evan Jones: “To heck with DoE. They would undoubtedly regard me as a deadly foe, anyway.”

I do not think so. They would do their best to judge your research proposal objectively. If anything, they would give you the benefit of the doubt because no scientist (as reviewer or as member of the review panel that judges the reviews) would like to be seen as stifling dissent and most scientists are contrarians and love it when someone holds a different opinion. Roger Pielke Sr was able to get funding to the end, irrespective of his close association with WUWT.

But, sorry if it sounds impolite, just being honest, you are (still) a lightweight and even with a bonus would never make it. The private sector may thus be a good option, if you not want to collaborate with experienced scientists. Collaboration has the additional value that it would get on the job training in doing science.

Evan Jones: “You seek the elusive fS.”

If “fS” are functions/equations that describe the physics of the temperature measured by a thermometers, then yes, that is what I am looking for.

I hope to be able to find the time to write up how your enormous heat sink would change the temperature measurements. As I described above, I do not think a heat sink would change the trend, it would only dampen short-term variability.

Evan Jones: “Then let us look at the 1998 – 2008 data. That is a carefully selected interval because it maximizes the cooling”

But why would the (physics of the) thermometer care about your carefully selected periods. Physical laws are always valid.

Evan Jones: “You got a fast car
I want a ticket to anywhere”

You got a fast car
I got a job that pays all our bills
You stay out drinking late at the bar
See more of your friends than you do of your kids
I’d always hoped for better
Thought maybe together you and me find it
I got no plans I ain’t going nowhere
So take your fast car and keep on driving

And many similar calls for civility on WUWT towards William and me. Much appreciated.

Evan Jones: “But you-all had concluded (with hot-sauce on it) that TOBS was the Achilles’ heel, and that the paper was falsified, hadn’t you? That was your confirmation bias.”

I actually wrote in the comments below my post on the Watts et al. (2012) manuscript: “If this [Time of Observation bias] is really an important effect, I do not see it as an excuse that Anthony Watts is not an academic insider. This is something one should check before publishing and I would see this as a lack of rigor. That there is an TOB in the US network is no internal secret, but known from the literature, for example, studied in Vose et al. (2003).

Thus we now have three reasons, why the technical problems may cause a difference in the trends of the raw data:
1. Time of observation bias stronger in rural stations.
2. More problems due to the UHI in the bad stations.
3. Selection bias (bad/good stations at the end of the period may have been better/worse before)

… Time for the Team Watts to start analyzing their data a bit more.”

Thus I wrote the same as you: “You were correct that those issues needed to be addressed.”

Like you wrote, it seems like it was no problem, but it was still wrong not to consider it. That is like studying the relationship between diet and cancer without taking smoking or exercise into account. It may not play a role for the cancer you are studying, but not studying it at all can easily lead to spurious results and is thus wrong.

Well, the announcement clearly does not fit to the claim that they only wanted to get some peer review. That could have been done after the holiday. If it was only getting some feedback you would also have expected some disclaimers at the publication of the manuscript to see it as very preliminary. And in case our naive colleague had contacted Watts before his congressional hearing, one would have expected Watts to request not to mention the manuscript at this stage.

However, we can disagree on non-scientific matters without immediately losing “credibility”. Especially, when one is kinda forced to defend the announcement because it was made by one’s boss.

Evan Jones, did you apply a constant CRS-MMTS shift? The same for every station?

This transition changed the protection against overheating of the instrument by solar radiation and heat radiation for the hot ground and cooling due to the cold ground. The size of this shift will thus likely depend on the insolation (time of year and latitude), on the shading of the old CRS and the new MMTS and on how quickly the ground heats and cools (and thus on the vegetation).

If you correct this transition with a variable correction, computed, for example, by comparing the means 5 years before and after the known date of this transition (preferably using a nearby reference station), does the effect then go away? With effect I mean that the trend is different for different quality classes. Alternatively you could have a look whether the effect differs for different seasons (insolation is likely the main effect and correlates with the others).

One thing I don’t understand about the heat sink idea is why it would lead to cooler readings in poorly-sited stations? (Apologies if this has come up already, there’s a lot of text here already.)

From my reading you’re saying that a heat sink would influence a badly-sited station in an anomalously warm period by causing the area around the station to cool slower and thus erroneously (at least for climate purposes) records Tmin warmer than the wider ambient temperature.

But there’s no such thing as cold energy for a heat sink to absorb then exude. On a cold day, why would the same heat sink cause the station to cool more rapidly rather than less?

[I think it would be helpful if EJ could write up his ideas in a document, rather than scattered blog comments. He’ll reply, no doubt, that he is indeed doing so: the paper. However, that’s taking a while to come out, so I really think a simple blog post on them would be helpful. If WUWT doesn’t want to host it, then blogs-are-free so EJ can get his own. There’s probably enough interest in this for him to start his own -W]

By a “specific situation” I meant the physical circumstances surrounding a particular station or stations where this happens. Something like: “Station XYZ is situated 5 meters from a Whatzit. The Whatzit is a [machine, building, animal, whatever] that works by [specific principle of operation]. This means that the hotter it gets, the hotter the air that comes out of a Whatzit. [Or the colder it gets, the colder the air that comes out, or other specific causal factor.]”

From physical arguments we usually expect that a “sink” will produce a damping effect and not an amplifying one. The lack of a physical mechanism is what is causing me and some others to be, erm, “skeptical”.

Aside (since wild rumour-mongering is fun): I speculated at the time, and I still think it likely: that announcement as actually meant to be something else; something that had to be pulled at the last minute. The paper pre-print was stuffed in at the last minute to cover the gap. That, too, is not to the discredit of the paper itself -W]

Dear, me, no, that was it all along. I was on the inside. You need to understand what was going on with us on that day I will always remember, when I first ran my reconfigured ratings results from my Leroy (2010) conversions. The trembling fingers as I framed the initial email. Anthony’s amazement and initial (appropriate) skepticism (too good to be true). Then the building burn as it all fell into place. This, after Fall, et al., no less. And after all those years and all those efforts. What can I say? You had to be there.

[That rather suggests you all got carried away. Will you agree, in retrospect, that the stuff KON quotes in #27 is well OTT? -W]

From physical arguments we usually expect that a “sink” will produce a damping effect and not an amplifying one. The lack of a physical mechanism is what is causing me and some others to be, erm, “skeptical”.

Then I will have to refer you to Leroy’s annex (see pages 57 – 59). He deals only with offset and not trend, but there certainly is no “dampening” in his findings. Or in ours.

You need to remember that USHCN results record Tmax and Tmin, only. Therefore, we are concerned only with heat sink effect at those two times.

In some of the surveys on surfacestations.org. Anthony has used an infrared camera. The angry heat radiating from the nearby sinks (both paved surfaces and walls of structures) is a sight to behold. This is not dampening.

Finally, in order to test whether our findings are a statistical artifact, I have binned the mesosite ten different ways, and looked at the isolated results of 9 regions. No matter how we bin it, in every single case, the well sited stations show trends than the poorly sited stations.

We are not cherrypicking one particular interpretation. We are shaking the baby in all directions looking for falsification. Not only because we know you will (and should). But because we “gots to know.”

Yes, buildings and pavements radiate heat (and radiate heat more than the grass that should be below the instruments (in regions were grass is able to grow) because grass loses part of its heat by evaporation). The comparison with an infrared camera suggests a larger influence as this heat actually has, because there is a shield around the thermometer to protect the thermometer against this (and solar) radiation).

However, that is the famous constant offset and your claim is that the heat sink increases the trend. At least in the last century. Not any more since we have the US Climate Reference Network.

And when I asked: “May I interpret the term heat sink as a substance that absorbs heat when the air is warmer and releases heat when the air is colder?”, your response was: “Yes”. Thus like Marco, I had assumed that your hypothesis was that the heat sink is doing the warming during the 1990s to make the temperatures hotter and hotter.

Maybe that also does not matter. Given that the trend bias is the same for all categories with heat sinks, not matter how small or big, close or far, the problem is likely not related to the heat sinks themselves. You know your stations best, what is different between the good and the bad sited stations, apart from the heat sinks?

The angry heat radiating from the nearby sinks (both paved surfaces and walls of structures) is a sight to behold. This is not dampening.

Although it will cause an individual measurement to be too high, it will indeed cause a dampening of the trend. To produce a warming bias in the trend you have to show that heat becomes disproportionately “angrier” over time.

The answer is in the data.

No. The answer is in the physics. The data point us to the right physical questions, and help us check our understanding of the physics, but the data themselves are not the answer.

Evan Jones, did you apply a constant CRS-MMTS shift? The same for every station?

I consider only the point of conversion during the study period and the amount of time of the conversion. Sometimes a station converted back to CRS, or is closed before the end of the study period. That is accounted for as well.

That is the way I am doing it now. I am aware of the limitations. So I had John N-G compare the overall results with NOAA’s MMTS-adjusted data, and it was quite consistent. (But see below.)

If you correct this transition with a variable correction, computed, for example, by comparing the means 5 years before and after the known date of this transition (preferably using a nearby reference station), does the effect then go away?

So I ask myself, “What would Mosh do?” Well, okay, he is trying to reverse-engineer, and that is where I depart from BEST (and you, for that matter). But if he were looking at it from my angle, I know what he would do: He would split the trend at the point of conversion and let the chips fall where they may. There’s your “conversion adjustment”, right there.

Now that I properly have my hands on the data I have been looking at that (Tmean, only, so far, and I know about the max-min whipsaw, so give me time).

So far, I am finding even less effect than Menne did. Anthony’s response has been, “go with Menne, then, because we want to demonstrate our results are robust, even with the cards stacked against us”.

But I will complete my analysis and see what we shall see. The effect of splitting the Tmax/Tim results will tell us a lot, more than Tmean. But I am not lead author and it is not may call. Good subject for a followup, in any case.

Speaking of which, I am already trying trend-splitting on raw data for TOBS flips and comparing with the adjusted data. The results are what one would describe as eye-opening. But I have only run a couple dozen so far, so I must withhold judgment at this time.

Although it will cause an individual measurement to be too high, it will indeed cause a dampening of the trend.

Will it? A fair question. So we look at the data and see. #B^)

To produce a warming bias in the trend you have to show that heat becomes disproportionately “angrier” over time.

I agree completely. To do that, we are looking at ΔS (S1 – S2). But I have to say that if you want to know what the effect on trend will be, you have to look past the theory and see what the data says. If the data is correct, and it disagrees with the hypothesis, then the hypothesis is wrong.

You know your stations best, what is different between the good and the bad sited stations, apart from the heat sinks?

The only things I have discovered are

1.) CRS stations have higher trends (hardly a surprise; the box itself is a heat sink), and the best sited stations are more likely to be CRS.

2.) The well sited stations are more likely to be located near cropland. Cropland shows quite a bit more warming than non-cropland. But Leroy does not rate crops as a sink, so we do not downgrade the rating for that. US surface is 19% cropland. 30% of U*SHCN is located in cropland. (The effect of cropland on trend appears far greater than that of UHI.)

If one adjusted for those factors, our results would be even stronger. But we do not adjust for those factors.

Which, by the way, makes your statistical result weaker. It adds another degree of freedom to your study. If you try enough different options, one of them may look statistically significant, but it actually isn’t.

It would make the statistical results even weaker if there were no stations remaining in the sample. #B^)

In order to address those concerns, we look at many subgroups. Undeveloped (i.e., no urban or cropland), MMTS-only, CRS-only, Cropland only, Airports only, urban only, etc.

We also look at the separate results from the nine NOAA CONUS regions.

In every case, the poorly sited stations have higher trends than the well sited stations during the study period. Every case.

Would you not agree this that greatly strengthens our overall statistical results?

Evan Jones on JoNova (I prefer not to comment there): “Does OZ not use automated systems for airports (and if not, why not)?”

If I am correctly informed the automatic sensors in Australia are placed in Stevenson screens (Cotton Region Shelters), just like the thermometers used to be. They did not put them in the typical small cylindrical cones not to influence the homogeneity of the measurements too much, to make the non-climatic change smaller.

So, Dr. Venema, you possess a “von”, do you? (That is more interesting to me than it might be to my fellow-Americans.)

The differences between these subgroups or the absence of differences can give hints on the cause of the trend differences or exclude certain causes.

There is some variation between regions, yes. Less than I would have expected though. It is interesting how two plots can look so much alike — and then you drop in the linear trends and it knocks you off your feet.

(And, yes, I know OLS is statistical porn. But we love it so. It is our meat and drink.)

Though maybe together you and me find it

So we may. Or we may not. But if to the extent that we can interact, our chances are improved. At least we will know where we stand.

The fruitarians are lazy
. . .Evan Jones makes a cameo appearance, saying “I hate and despise homogenization with an ice-cold passion” which is jolly fun, but he doesn’t tell us why. But it sounds like he’s a fruitarian.

I. Am. Not. Lazy.

[I don’t think you are. I was commenting on those lazy ones who inhabit JoNova’s blog -W]

If physics is not producing our results, I am a loss to what is. But that is a school of magick that exceeds my limited capabilities.

@VeeV
Thanks for the info re. automated stations. I have observed that CRS units warmed significantly faster than either ASOS or MMTS during our study period. I think that this is because the box is a heat sink, in and of itself.

It may therefore be that the cooling observed in the raw data of some of those stations may be exaggerated as well?

Evan – Different types of temperature sensors (PRT, LIGT, TC) have different response times. Even within subgroups of the same type of sensor the response time will vary based on certain physical characteristics (usually mass). Have you made any attempt to see if there is any relationship between response time and raw data and/or trend?

But there’s no such thing as cold energy for a heat sink to absorb then exude. On a cold day, why would the same heat sink cause the station to cool more rapidly rather than less?

Ah. I am not speaking in terms of offset. I am speaking in terms of trend.

A station in a cold environment will still be affected by a nearby heat sink. But it will not be as affected as in a warm environment. Therefore if there is a warming trend, the Delta Sink effect will be greater at the end than the beginning of it. So there is a spurious increase in the warming trend.

When you have a cooling, trend the effect begins to undo itself. There is still a Sink effect at the end of the interval, but it is a smaller effect than it was in the beginning of it.

So as a station reverts to its prior norm, so does the Delta Sink effect. It will have warmed too fast. Then, when/if conditions return to its pevious state, it will cool too fast.

What goes up, must come down.

You must be careful not to confuse or conflate S with ΔS. During a warming phase, it is a positive number. During a negative phase it is a negative number. But S, itself, is always a positive.

[I think it would be helpful if EJ could write up his ideas in a document, rather than scattered blog comments. He’ll reply, no doubt, that he is indeed doing so: the paper. However, that’s taking a while to come out, so I really think a simple blog post on them would be helpful. If WUWT doesn’t want to host it, then blogs-are-free so EJ can get his own. There’s probably enough interest in this for him to start his own -W]

I am better cast as a subordinate. A man’s got to know his limitations.

The comparison with an infrared camera suggests a larger influence as this heat actually has, because there is a shield around the thermometer to protect the thermometer against this (and solar) radiation).

If I recall correctly, MMTS came out as more accurate than CRS. So I trust MMTS trends more than the latter. But there does appear to be a step change (a significant negative at Tmin; an even larger positive at Tmax, resulting in a mild positive at Tmean), according to Menne (2009).

I wish to confirm or falsify this by spitting the trends at time of conversion and observing the effect. But I have not completed this task. When looking at my station graphs, I confess I cannot see much of a Tmean step change at all. Looking, however, is not numeric. So I must do the footwork.

Evan, are you arguing that the point of the big fuzz Watfs made was to attract critical comments to challenge your confirmation bias?

If so, allow me to laugh. Ha! I don’t believe it one second.

If not, what have you learned from Watts and his “major announcement”, and this being followed within a few days with someone pointing out that you didn’t even do something as trivial as correct for T(OBS) ?

Evan, are you arguing that the point of the big fuzz Watts made was to attract critical comments to challenge your confirmation bias?

If so, allow me to laugh. Ha! I don’t believe it one second.

Laugh as you may. (Make it more than one “Ha”, as you will.) If there were more laughter in this world, it would be a better place. Good for the digestion, too.

But I crave your indulgence.

To the point, Anthony said he was doing the same thing as Muller. (We discussed this prior, so I know.) Muller said (and Anthony quoted) that was customary in his circles to issue a pre-release of a paper in order to elicit independent review:

The pre-release of this paper follows the practice embraced by Dr. Richard Muller, of the Berkeley Earth Surface Temperature project in a June 2011 interview with Scientific American’s Michael Lemonick in “Science Talk”, said:

I know that is prior to acceptance, but in the tradition that I grew up in (under Nobel Laureate Luis Alvarez) we always widely distributed “preprints” of papers prior to their publication or even submission. That guaranteed a much wider peer review than we obtained from mere referees.

There you have it. Cut and pasted straight from the “hoopla”. Did you suppose he did not mean this?

And elicit independent review, it did. Far moreso than if it had been done “quietly”. And I have avidly taken advantage: To defend where defense was appropriate. To reconsider where reconsideration was appropriate. To modify where modification was appropriate. It has been a long journey. There is more ahead.

And, yes, we wanted to be heard. As widely and and loudly as possible. And, yes, it was a rallying cry and a shout-out to the troops. Our volunteers. The hundreds who spent so much time, so much effort, and so much money (entirely uncompensated) to provide us with the station photographs. They had waited a long time. They needed this. We owed them.

You can scoff at all this. I understand, really, I do. But I will not speak against it. You need to consider our position, especially after Fall (2011).

Those results did not support our hypothesis (at least not for Tmean). We could have suppressed it. But that is not the way of the scientist. So, of course, we released. We could do no other; it never occurred to us. And it is not too far to say that our emotional state was not terribly unlike von Manstein’s after Kursk.

So, when we turned the corner, shout we did. And I shouted along with all the others, too. And shout, I would, if we had it all to do over again. I have no regrets and I have no apologies.

So there was that, too. And why not? It helped us tremendously. It helped me, personally. It gave me the review I needed. It gave me the voice to be heard elsewhere.

Everyone had something to say. The plaudits were swell. But it was the vehement counter-reaction that was the gold speck. There was the value. Just as Anthony had intended. And here we sit. Quod erat demonstrandum.

Besides, I do not tend to speak ill of any man. Not my style. And do you think me incapable of that? Think again. I was raised with a white knife in one hand and a black knife in the other. And you will never know which blade carries the poison until I just barely nick you with it. My mother was a top communist, a dear friend of Earl Browder, and my father was a college professor. I am a product of New York City private schools and the Ivy League. Trained in deconstruction and the dialectic. My sport is fencing. Capable? I have a controlled ferocity burned into my soul that you cannot possibly imagine.

Yet my personality is not that of a Manstein, but that of a Kleist. I defend my staunchest critics, and in venues where such defense is not necessarily entirely welcome. My choice.
But just because I am a man of peace doesn’t mean I do not carry sudden death in each hand.

So why would you require me to speak ill of Anthony, who has done so much for me and has afforded me so much opportunity? I will not. And I can assure you that if I did, I would not stop there.

You are welcome to dismiss me; that is entirely your choice. But should you not, you must accept me as I am.

It’s alright. You had reason to want to know, so I told you how it happened. How you judge it is up to you.

But consider this from my angle. We were confident of the results. I really have tried to get it right.

I never did have much of a problem with iconoclasm, anyway. besides, you make it sound so easy, We are reasonable, men and expert in out field. Just drop it off and we’ll get to it Real Soon Now, followed, after a suitable interval and a light dismissal.

Sorry. Not happening. We were determined to obtain a serious scientific response, and I thank those here for that.

This was and is a serious issue. If it weren’t, you wouldn’t be bothering about it.

Anthony uncovered and it (building on the work of Dr. Pielke, Sr.) and the volunteers documented it. Our opinions on the matter went back and forth in those earlier time, as these sort of things always do.

So, no, we did not choose to vanish before we had even appeared. We regret that we cannot oblige.

Besides, we’ve had the best independent review — evah.

Look at all the ground covered in just this one thread. And when they are asking questions during review of the work that the professionals should have gone about years ago, I’d just as soon have some answers, if the lords and ladies will permit. It is what it is.

My side of the bargain is to listen to you, answer you as best I can, and consider making changes based on the above. I take that seriously. I have listened, I have considered. I have made changes. Major ones involving major effort. That’s how I choose to play it.

And you also know if you want to hash it out with me, I will not insult you, I will not demean you, and I will not tell you to read a book. (Just an annex, maybe.) That’s not in the implicit contract.

That’s extra.

I am acutely aware of the high caliber of the experts here and elsewhere who have years of directed and devoted discipline and training followed by years of experience in the field. And also of those with less training but good analytical skills and a question. I’ve been on both sides of the desk, too, in my travels, and if someone wants to know something about this, my impulse it to tell them. But you know that, already.

Three of your words can cost me a hundred hours, and I’m rounding down. Those are hours I am and have been willing to spend.

So now that we have obtained your attention in much the same manner that the buck sergeant got the attention of the army mule, we are able to answer some questions and question some answers.

So without the pre-release — and the unseverable said hoopla — we wouldn’t be here now. You know it, and I know it. It is a less gentle world than I would prefer, and I need to live in it. But I will play a straight game.

I suppose pissing off McIntyre by putting him down as a co-author without his permission was also done to simply stir the pot and make sure that many eyes would review your unsubmitted but widely publicized paper-that-apparently-wasn’t-quite-a-paper-yet?

I suppose pissing off McIntyre by putting him down as a co-author without his permission

You mustn’t presume such things. Anthony (with my hearty approval) took him on as a co-author mainly to check my work and make sure I had done my sums right. Which he did. And he is not pissed at any of us. Certainly not with Anthony. And if he is pissed off at me he has failed to make me aware of it.

And of course it “wasn’t quite a paper”. That’s why we made the pre-release. That was the point. I discussed this with Anthony at some length, prior. If it had been, we would have submitted for publication then and there.

We felt were in a strong position, but we very much wanted to know what the criticisms would be. We were (and are) well aware that peer review would be very exacting. I make no objection to that. We are making extraordinary claims, and extraordinary claims require extraordinary proof. We both know that. You must forgive us for not wishing to remain not-quite-ready.

Although Anthony (with my cooperation and approval) held the tension in his hand, kept people guessing for a few days, and then issued his statement, that was all he did.

He didn’t line up a publicity team. He didn’t set things up with the New York Times. But he has a standing in this field, whether you like it or not, and take full advantage of it, we did. And would again.

But that’s all. It was you guys and members of the press who took it up provided the actual publicity. We held your feet to the fire, perhaps, but I have no apology for that. We wanted questions and answers. We did what we had to do to get them.

And, yes, we got them, in the form of a very severe series of independent reviews. Far more severe than what peer review I have previously encountered, at that.

I did not discuss all of them everywhere, but you can bet I was following them. And they have been extraordinarily valuable to our efforts.

Please understand, we respect the expertise and experience of those who are involved with temperature measurement and adjustment. We have taken a different approach. The NCDC/USHCN oversamples heavily, and their metadata over our study period (all much to their credit). We wanted to isolate the heat sink issue in a manner that did not get conflated with that of adjustment. So rather than run afoul of extraneous factors, we decided to drop the stations that have issues other than microsite and use raw data from those remaining.

So we needed your reaction. We got it. Thank you, and I mean that in a truer sense than you might think. I have done two thirds of my massive amount of work as a direct result of those reactions. So I could look where to look in the areas I had stopped looking.

We take your criticisms, suggestions, and insights seriously. We appreciate the efforts you have gone to. We do not ascribe ulterior motive and dismiss. Instead we strive to address them.

For one, we do not even use raw data anymore. We apply MMTS conversion adjustment consistent with Menne (2009 & 2010), which works somewhat against our premise. For another, I took a much closer look at airports and cropland (greatly assisted by the recent and continuing improvements in Google Earth). And that is just scratching the surface.

But we had to get your attention first. You need to appreciate this. We are not your enemies. We are seeking assistance. And we need — I need — the input of the professionals insofar as we can obtain it. If aggressive intellectual panhandling is the worst charge against us out of all that, I am willing to cop that plea.

<In common parlance we call these oceans, sometimes lakes.

Why hullo, Dr. Halpern. Please feel free to make any inquiries you wish. I will be pleased to answer as best I can.

Yes, bodies of water are classified as extraneous heat sources by Leroy (his terminology), unless “typical of the area”. But so are paved surfaces, structures, and active parking lots (paved or not).

@VeeV
Do you have any reaction to the notion of splitting trends as an address to MMTS conversion, or even TOBS flips?

It seems to me to be the best and most logical answer to the issue. Clean, simple, and self-contained as opposed to the somewhat Byzantine methodology I have see in the literature.

The metadata is quite precise as to the timing of these conversions. You have a potential jump, so you simply bypass it. The size of the jump, whatever that may be, simply comes out in the wash. What are the weaknesses to this approach, as you see it? (No, I haven’t done this yet, just a glance or two, so I don’t know what the results would be — yet.)

Does anyone know how to concatenate the USHCN text files? There is a separate file for each station. I wish to assemble it all into a single file. That would save me many hours of tedious work going forward. I have Tmean already, of course, but I am anxious to do the rest, and it is quite a pile.

Evan when you say “we respect the expertise and experience of those who are involved with temperature measurement and adjustment”, does that “we” include Anthony Watts, and does “those” include the people at NCDC?

If the answer to both is “yes”, may I ask in what world someone would accuse people he supposedly respects of deliberately cooking the books? And in what world would someone allow, on his website, allow commenters, largely unopposed, to make such claims on a regular basis (see for example the recent threads on some stations in Australia)?

Evan when you say “we respect the expertise and experience of those who are involved with temperature measurement and adjustment”, does that “we” include Anthony Watts

No.

Although he is a decades-long meteorologist, and currently has four peer reviewed publications, he is, essentially, like myself, an academic outsider. By analogy, he would be an ex-NCO, as it were, with good basic working knowledge and a wide familiarity — and a field commission for commendable action in the face of adversity. A fact that I both respect and admire.

This, while I am, essentially, a buck private, entirely self-trained, with only a very narrow (but relevant, I think) expertise; I know that, too (and am well aware of its accompanying limitations). I march well in inclement weather.

But neither one of us are West Point men, and we both appreciate that fact.

and does “those” include the people at NCDC?

Yes.

It especially includes the people in NCDC. Folks like Dr. Menne (my prime nemesis in all this). Gosh, how I wish he would come here so we could discuss this. Tallyho!

Also, folks like Dr. Venema, who was on the inside on the development homogenization (my opinion thereof indicated as part of a footnote of this post). So, alright, I disagree with that very approach, not to mention some of the applications. So wouldn’t that make him the very person I would want to discuss the matter with?

And he treats me so well, so decently, with such outstanding noblesse oblige. “Von” by name (unimportant), “von” on the field of battle (the acid test). Even as I savage his baby. Imagine my respect for that.

Besides, I don’t need more people who agree with me. I already got those. I like ’em, sure. But it’s those who don’t that I can learn more from. And if I have fond hopes of winning them over (even partially), I never, ever forget that the reverse is true.

I ask in what world someone would accuse people he supposedly respects of deliberately cooking the books?

Asked and answered. Such is the way of the wicked world. I know it. You know it too, I think; you wax rhetorical here.

At any rate, I vehemently object to it, there and elsewhere. I look down my nose upon the political froo-fraw (when I am not simply ignoring it). Politicians will pigpile; it’s what they do. They do what they do; I do what I do. (McIntyre will delete posts containing the 5-letter f-word, for no other reason than that. He is a rare, if not unique, exception. But I do not delete.)

Besides, in the long run, he who lives by the executive order, dies by the executive order. This is not going to be decided in the smoke-filled rooms. It will be decided in the journals, in the labs, in the field. Politics will follow; it is merely a lagging indicator. Do not mistake the brightly colored tail for the dog.

Yet I will not censor such nonsense, that is not may way. I will and do comment, and sometimes with a spot of acid to counter the soporific on the emperor’s blade. When I am not too busy ignoring the whole wretched mess. WUWT is no worse than most, and better than some. Besides, under our current circumstances (fueled by fossils?), it is not the size of the desert that is at issue. It is the number of oases. Dr. Venema indicated this earlier, though perhaps not in so many words.

Free speech and open inquiry are not defined as that which happens to have tickled my fancy last Tuesday. There are always limits. Those limits are not defined by what displeases me today at 3:00.

So I won’t censor that, either, but I will squash it like a bug when it comes to my notice. When I moderate there (when I have time) I almost never delete any post for any reason. But that doesn’t mean I will let it slide.

Never ascribe to fraud that which can be adequately explained by confirmation bias. Confirmation bias is like other things beginning with the letter a: we all have one. To deconstruct, one’s confirmation bias is (largely) determined by where and when one stops looking. I accept that. There are only so many hours in a lifetime, after all. It’s a human limitation. It’s why I require help. I can neither build nor burn Rome in a day.

The only thing that moves me near actual anger is when someone on my own side starts talking in terms of prosecution and jail. Both sides do it. It is a damnfool attitude, and dangerous, at that. They should maybe read some history, already. And talk about a sword that cuts both ways. Today we try you? Tomorrow you try us. Don’t go there. Don’t ever go there.

When people do this, it denotes a lack of sophistication and intellectual discipline, not necessarily that which is beyond redemption. This can be remedied by a spot of education (not excluding that of the school of hard knocks). In such cases, I will attempt to set them back on the path and enjoin them to sin no more. Some of them learn. Eventually.

Being wrong is not fraud. Being right today does not mean you will be right tomorrow. Or that you were right yesterday, for that matter. My set of beliefs today only partially overlaps that which I had coming into this thing. We learn, err, correct, move on. Being always aware that one must at backtrack any time. Some of these hombres forget this, if they ever really knew. I supply their Day of Remembrance. [As in, WHAM! Now go forth and sin no more.]

This does not come easy. There are occasions when I will forget myself. Willfully, too, and not without malice. I am always in jeopardy, thus, as much as any man. Sometimes I poison, slow poison, with all the guile, subtlety, and kindness at my command. This is cruelty. I am the long sobs of the violins of Autumn. I can wound your heart with a monotonous languor. I will. I have.

Make no mistake. I am no pacifist. I never walk without my weapons (I don’t mean the physical variety), and I will not surrender them. I cannot; they are a part of what I am, what I have been made. You saw them, too, when I put my hand to my belt earlier on, regarding Anthony. I am not ashamed of my abilities, such as they are. I am sometimes, in retrospect, ashamed of my actions, and deeply so. Men are men. War is war. No hand is without blood.

And in what world would someone allow, on his website, allow commenters, largely unopposed, to make such claims on a regular basis

This world. The one we have, and we must first survive in it before we can prosper. When one is assailed, the blood runs hot. I am loathe to judge. I like to feel that Dr. Connolley, in his finer moments, would agree.

There are places I do not post, not where where I am challenged, but where I feel I will be silenced. I will post where I am permitted to be a free spirit — by my own arcane definition. Including places where others are stifled or badly used: I am not my brother’s keeper. I have too much respect for him for that, and, I do not have the time. Yet I will not depart to the Land of Nod.

Gee, another long post in which your first answer already would have been enough. “we” does not include Anthony Watts. Done. No further response needed.

Two things, though:
a) considering Nick Stokes recent experiences, I don’t think I will last long at WUWT. He is far better able to remain untouched by the vitriol and abuse, and still manages to get moderated.
b) However much McIntyre moderates the “fraud” word, he has no problem accusing a lot of people of all kinds of scientific misconduct. He even ultimately took it to court (re Briffa)…and lost.

I can’t control the behavior of the posters on WUWT. If you are reasonable, keep a cool head, wave off the first volley of shots, and adhere to site policy, I’ll predict you’ll do okay. We do have skeptics there and they are not all Nick Stokes.

But you know the drill. You know the hostility that persists on both sides. Dr. Connolley’s commenters are as well. and he can’t help that, either. I struggle with that because it impedes my progress. I need the other side. It gets closed off.

As for Mac, the whole point of his blog is picking apart statistical derrings-do and pointing out what he thinks are errors and inconsistencies. When he gets a cold shoulder, he’ll put that up as well. Scientific behavior is largely about response. But he is willing to listen and respond. And the fact that he keeps a tight reign on his posters in notable, even though it is not how I operate.

Evan Jones: “So, Dr. Venema, you possess a “von”, do you? (That is more interesting to me than it might be to my fellow-Americans.)”

No, when my older brother went to university, my grandma protested: Venema’s do not go to university.

I am actually from a relatively poor background. Venema means bog worker in Dutch. The Dutch welfare state made it possible for me to concentrate on my studies and to make something out of my life. (Something which is nowadays unfortunately already a lot harder again.)

Evan Jones: “Thanks for the info re. automated stations. I have observed that CRS units warmed significantly faster than either ASOS or MMTS during our study period.”

Does anyone know if there were increasingly more calm wind situations in the 1979 – 1998 period in the USA-48?

Evan Jones: “I think that this is because the box is a heat sink, in and of itself.”

To me a heat sink is an object that absorbs heat (and releases it later). The heat capacity of a CRS is rather limited, thus I would not consider it a heat sink.

If you call any source of temperature bias a “heat sink”, you may be right. CRS are not perfectly white and are thus warmer than the air when the sun is shining bright and the wind is low. When the air flows through the shelter it is warmed by the warm walls.

This is one reason why I would theoretically expect modern automatic weather stations (AWS) (if properly maintained (so that they stay white)) to record lower temperatures. They are mostly mechanically ventilated and thus should have less problems when the wind is calm. Unfortunately there is no evidence for this yet on a global scale. (In America the bias (CRS to the MMTS AWS) is about 0.2°C, but that is just one case.)

Governments rather fund climate change impact studies to give them policy advice. More blogs like WUWT would be good for me, especially in Europe and especially if they would up their act and stop destroying their credibility by publishing so much utter drivel.

William: “I think it would be helpful if EJ could write up his ideas in a document, rather than scattered blog comments. He’ll reply, no doubt, that he is indeed doing so: the paper.”

There was such a thing. The Watts et al. (2012) work page. It seems to have been removed. I guess Anthony Watts did not like review as much as his press release suggested.

Good that Even Jones seems to have licked some science blood and does appreciate critique and realises this can only make his case stronger (if he is right).

Evan Jones quotes Muller saying: “I know that is prior to acceptance, but in the tradition that I grew up in (under Nobel Laureate Luis Alvarez) we always widely distributed “preprints” of papers prior to their publication or even submission. That guaranteed a much wider peer review than we obtained from mere referees.”

The problem these days with social media is that the boundaries between personal/professional communication and mass media is getting leaky. I guess, Anthony Watts as blogger had no other real option as publishing the manuscript on his blog. However, one could have expected a clear disclaimer that the state of the manuscript is very preliminary and not suited for mass media or congressional hearing. At least if his aims were scientific, not political.

Evan Jones: “Do you have any reaction to the notion of splitting trends as an address to MMTS conversion, or even TOBS flips?”

That would be perfect. I wanted to keep my suggestion simple, for you to implement and for the reader to understand what my request was.

Doing it in a more advanced way is likely somewhat more accurate. The main improvement would be the use of a reference series and performing these computations on a difference time series.

Evan Jones: “folks like Dr. Venema, who was on the inside on the development homogenization”

I am not. I only validated and studied those methods. The next three years I will also work on developing homogenization methods for global datasets. NOAA needs to get some competition. :-)

Evan Jones: “Even as I savage his baby. Imagine my respect for that.”

Not necessary. I know people at WUWT do not tend to believe this, but that is the normal work of scientists. At least if you “savage” ideas in a respectful way and with good arguments.

When I validate homogenization methods I hope to find they do not work. That would be an interesting result. I cannot help it that I found that they worked as advertised. On WUWT that makes me the enemy. On the German blog EIKE that makes me a researcher that is close to the IPCC (IPPC-nah). At the time, I did not know anyone, who was also writing on the IPCC reports, as far as I know.

Just wait for my next article; I think I found a problem with homogenization. Which I will blog on after peer review. :-)

Evan Jones: ” If you are reasonable, keep a cool head, wave off the first volley of shots, and adhere to site policy, I’ll predict you’ll do okay. “

No, that is not enough. You should also be very friendly about WUWT and Anthony Watts elsewhere. At least that is how the moderator (AW?) explained it to me. (And I always thought moderation was to keep the discussions civil, not as punishment for behaviour elsewhere.)

Otherwise, you will also be put under moderation. Even if you are main expert that blogs on the main topic of WUWT. Being under moderation means that your comments will be published some hours later, when dozens of new comments have appeared already and hardly anyone notices your comment any more.

Something else, Evan Jones, do you know if something ever came out of Watt’s first science project’ His study of the influence of white wash and latex paint on the temperature measurements of Cotton Region Shelters. As far as I know, he only published one post on that, with the trivial results that no paint whatsoever (what is not used) has a warming bias.

The political bias of WUWT and the discontinuation of this project suggests that the change in paint produces a cooling bias. That would be scientifically interesting. Scientists are interested in all sources of bias, not just the politically convenient ones.

If someone else with a large flat lawn in his garden reads this: could you maybe repeat this experiment? Some citizen science.

Victor, there is bad blood between you and Anthony. You have have both cut into each other in such a manner that it makes we wince even to consider. The knives you carry are not entirely dissimilar to mine. You need to fully appreciate the pain they can inflict, especially when applied to a sore spot.

Do you remember when you said, rather offhandedly, that you had to get the Ph.D. in order to be allowed to continue your work? And how Anthony didn’t quite understand? But I did. These things are looser here in the States. And everything Anthony has achieved is a direct product of that looseness, and the American go-getter attitude of the volunteers. You come from different worlds. Different standards of pain, you know.

What is a mild jibe to one man in one subculture can be a deep and terrible wound in another. The reverse situation is true. Things Anthony considers mild or unimportant you find hurtful and unfair. The closer related cultures are, the worse it is, up to a point, because your guard is down. Before you know, you are in one of those IPCC positive feedback loops.

You also must also realize that “you must not be very unfriendly” is not quite what I would quite call the equivalent of “you must be very friendly”.

You are a professional in the field under consideration. So you are held to a higher standard than the normal poster. It may not be site policy, it may not be fair, it may not be democratic and it may not be egalitarian, but it’s reality. And if you ask yourself, you will also know why it is reality. it is the human condition. You possess actual power in this context, and therefore you are stuck with an increased standard.

Call it an adjustment. Consider it a mild form of flattery.

As for me, I’d just like to see you two homogenize this thing out.

Does anyone know if there were increasingly more calm wind situations in the 1979 – 1998 period in the USA-48?

Interesting from a tactical viewpoint, too, as well as from your angle..

To me a heat sink is an object that absorbs heat (and releases it later). The heat capacity of a CRS is rather limited, thus I would not consider it a heat sink.

Whereas I might consider it a limited heat sink. So what we would do it look at both and see if there is a difference (significant or not) between CRS and MMTS. That way at least we can at least look at it again.

Those boxes are heavy. And some of them have funky roofs (that I don’t rate for).

The ones I’ve personally surveyed had beautiful boxes, excellently maintained (Cooperstown and Mohonk Lake). But others that I have seen photos and street level images are truly wretched. There was one with a bird’s nest in it. Much of the paint wears off a box, which makes matters worse.

The best way to find out how CRS and MMTS compare is simply to compare the two.

Really glad you like the caveman approach to equipment change. It’s something I can actually do. “It’s what Mosh would do”. Problem being that Mosh will never rest while a single trend on this earth remains unsplit. To a man with a hammer, every problem looks like a nail. I could live with that, but I think he has started seeing nails.

If you have no metadata, then you ain’t got no metadata. You can’t reliably infer it. And the longer you do, the more you fall in love with it. Before you know it, you are not only inferring the missing metadata, but you are letting the inferred metadata boss around the metadata that already does exist. A slippery scientific slope.

I think we are finally learning our lesson about the importance of good metadata. But that only helps us going forward.

Since this paper compares the differences between the various temperature shields and provides data for nighttime and daytime differences, data after varying local heat sinks, and data under varying wind conditions I would think it would be be a piece of the prior literature you would already be completely familiar with.

When I validate homogenization methods I hope to find they do not work. That would be an interesting result.

Alright, you will know about all of the following issues. What I see here is a conflict between theory and application.

I cannot help it that I found that they worked as advertised.

I feel your pain. Try a flick of the elbow during the riposte.

But here’s what I take away from it. In a sample unbiased by an independent variable, homogenized works as advertised.

The idea is to improve the accuracy of all. It is used effectively in the mining industry and in engineering. The problem is that it does so at the sacrifice of the accuracy of each.

But, when satan doesn’t have the fix in on the dataset, yes, it does perform as advertised. And yes, its overall results may even be more accurate.

Now, I know you were constructively unaware of the siting problem. You are not to blame. I am sure you looked for systematic biases, both jumps and trends.

Microsite was off the radar screen for you guys, even if it had originally been on it (possible, I guess). Was that word uttered even once during your entire procedures?

But why should it have been? And if it had been, you had the work of Menne (2009): Word up, dudes — no trend bias. If the microsite rating remains the same, there will be an increase in offset, but no bias in trend.

Then fate threw you a wicked curve that caught the outside corner at the knees. You only had one initial hint. You had a number of stations that had lower trends than the others. If you had looked for a commonality, you might have found that a disproportionate number were far removed from anthropogenic influence. That might possibly have led you to do another rating comparison, but this time using Leroy (2010).

Lot of “mights” there. Don’t think I don’t get that.

But even so, you would have had to located a near-invisible the lock you didn’t even know existed, and pick it. And you were juggling so many other factors at the time. Believe me, I understand how these things can occur. I was too harsh earlier.

Whereas I knew where the lock was and had already waxed the key.

Assuming Leroy was correct, the logic of his findings suggests an effect not only on offset but on trend if you really think it all through.

The suggestion that the offset bias is less in winter months than in summer is the tip-off, the red flag. If you had gone there, the rest would have followed.

In any rate, there is a bias, and just the sort of gradual, steady trend bias that slips through the cracks and hides in the MoEs,

Your sample set has a systematic bias (microsite) that carries over to a severe effect on trend. Siting Matters. And the effect is disastrous.

What

And in this case, homogenization also performs as advertised: It says it “performance not guaranteed under conditions of a sneaky independent variable biasing trend,” right there on the warning label between the disclaimer and the skull and crossbones. You can’t miss it if you carry a magnifying glass. You know: It’s the part they say really fast at the end of the radio commercial.

So my specific gripe is not with you and your work (other than the aesthetic rebellion of my sensitivities). It is with the NOAA, which knew about the issue but failed to detect its significance. You didn’t let them down, they let you down.

Since this paper compares the differences between the various temperature shields and provides data for nighttime and daytime differences, data after varying local heat sinks, and data under varying wind conditions I would think it would be be a piece of the prior literature you would already be completely familiar with.

You are quite right to bring up Hubbard. There’s also more recent Yilmaz et al., which examines the same thing, but not the sensor shieldings, just the microsite. Not to mention Pielke, Sr.’s work.

But none of those papers don’t do trend. Offset only.

I know about them because we are looking at equipment conversion, and comparing the trends of different equipment is part of the analysis. But I don’t do the physics. I can’t do the physics. My role is to perform the data analysis.

Hell, if I wanted to I could do all the data in raw and slap on the MMTS pasty to the aggregate, citing Menne (2010). But I think I can figure out what the real story is without that: Do a revers-Mosh on the trend. Then you get your answer in trend. As measured.

Our physicist co-author thinks this factor is easy to nail and he does know about the Hubbard paper. I’ve framed the hypothesis. That’s my bag. He has the thankless task in filling in the actual science.

No, when my older brother went to university, my grandma protested: Venema’s do not go to university.

Interesting, And another example. I wonder if anyone’s gandma ever said that over here. I felt just that hint of culture shock again. The GI Bill changed America, and there was this wonderful opportunity for everybody’s last name to go to college free subject to flunking out. (That’s how dad got off the dirt farm).

My own folks were lower but solid middle class (Civil Service did not pay well until much later), but not poor, and with them it was either be educated without a fuss, or make a fuss and be educated anyway.

One thing I want to be clear on. I am making direct observations. That’s my bag. If I want to find out what CRS trends are relative to class, I look at that data. Then I know how CRS stations compare with MMTS.

My personal objective is to avoid the need for citations — whenever possible. And I think just maybe I have figured out how to shake free of Menne, as well. I’d love to not cite him.

I never did understand why some damn cite is always considered better than actually finding out. The data is there. The metadata is there. The dataset is pristine. Science: Why would I want to look at pictures of it when I could be doing it?

This is incorrect. Hubbard et al has hourly, daily and seasonal data so that the effect of various solar angles and nighttime/daytime differences can be discerned. Numerous trends are identified.

The effects of microclimate are probably best summed in these two excerpts:

“b. Solar irradiance inside the shields over surfaces with different solar reflectivities

As the underlying surface solar reflectivity increased with changes from black, to grass, to aluminum, and then to white surface, the interior solar radiation generally increased. The trends in TSRR% with time were generally the same from one surface to another, with the exception of ASOS and CRS above aluminum (Fig. 5). For the black surface, the TSRR% had distinct differences among the Gill, MMTS, CRS, and ASOS shields (Fig. 5a). Above the highly reflective white surface, the TSRR% of the Gill, MMTS, and CRS shields were the highest encountered.

Increasing the solar reflectivity from a black to a white surface for the ASOS had little effect on the daily ISRR%, due to the solid barrier represented by the ASOS structure and the black painted inner surface (Fig. 6a). The daily ISRR% inside the Gill, MMTS, and CRS had almost the same linear increasing rate with the increase of surface solar reflectivity….”

The difference between the average daytime inner wall surface temperature and the average temperature of the sensor varied from 238 to 148C (Fig. 7). The temperature difference distribution of each shield follows a Gaussian distribution, with the modes around -0.5, -0.5, 0.0, and 1C, and the average values -0.55, -0.26, +0.01, and +0.88C, respectively, for the Gill, MMTS, CRS, and ASOS shields. The negative average temperature difference for the Gill and MMTS shields reported here is in contrast to other studies that state that the radiation shields could heat the air or air temperature sensor resulting in a shield heating error (e.g., Richardson 1995; Tanner 1990; Tanner et al. 1996). Based on the absolute average difference between the shield inner surface temperature and sensor temperature (Fig. 7), the infrared shielding effectiveness for each shield during daytime was ranked as CRS > MMTS > Gill > ASOS.

During the nighttime, the distribution modes for the difference between the inner-surface temperature of radiation shield and the sensor temperature were approximately zero (Fig. 8). The average values of temperature difference were -0.20, -0.20, -0.12, and -0.07C, respectively, for the ASOS, CRS, MMTS, and Gill shields….”

The text only states the average values, but the graphs show the full distributions.

Evan Jones: “One thing I want to be clear on. I am making direct observations.”

Huh? You’re out in the field collecting data? I thought you were crunching numbers.

“My personal objective is to avoid the need for citations — whenever possible. And I think just maybe I have figured out how to shake free of Menne, as well. I’d love to not cite him.

I never did understand why some damn cite is always considered better than actually finding out.”

On its face this seems both childish and ignorant. Why would you ‘love’ not to cite someone? Do you understand the purpose of citations? They are there to both give credit to those who did earlier work – work which undoubtedly informs your own – and to show that the author is conversant with the science and understands its terminology and history.

A citation is not a substitute for doing your own work. Often times a paper may be cited to *disagree* with its conclusions. But often the previous work, even when the present work disagrees with it, was important in defining the subject or raising specific issues.

Yes, there are times that a citation is shorthand for – “I’m not going to rehash all of this – go read XXX.” This is usually to save everyone’s time. There’s no need to rewrite an already published paper just so that we’re all clear on terms.

Science has learned over time that there are very few idiot savants worth listening to. There are a million blogs with a million different crackpot theories; climate change, economics, politics – you name it. If there is a common thread to these widely disparate sites it’s that the writers very rarely are experts in the subject area. An expert in the subject area *knows* the relevant literature. Within his field he may hold minority or majority views, but he knows the arguments set forth by both. Citations show that the author does indeed know his subject.

In #72 above the sentence reading: “The difference between the average daytime inner wall surface temperature and the average temperature of the sensor varied from 238 to 148C (Fig. 7). ” is incorrect. It should read “varied from -3 to +4C”

Yes, a variance of 7C is interesting. It is data our physicist can apply. But I want to zero in on the citation argument.

On its face this seems both childish and ignorant.

I know. I know. That’s what everyone always says. I’ve been hearing variations on that tune since around 1980. And I have been rebelling against it ever since.

When I was doing my thesis, I was expected to have at least one citation to justify every paragraph. The one actual insight I took away from the whole damn mess did not have a citation and therefore had to be removed prior to approval.

And the entire rigamarole had no real intellectual value without the insight, so far as I was concerned. No one got that even a little, least of all my supervisor.

Why would you ‘love’ not to cite someone?

Because I’d love to figure out the effects of MMTS conversion myself — without having to cite anyone.

I have spent over a year carefully preparing the perfect dataset for the purpose, I have hit on a simple, direct, empirical method.

Citing Menne is risk-free. his numbers don’t hurt us. Everyone nods solemnly. “Ahhh, Menne.” It’s cool to be able to say you are citing part op a paper whose main point you are refuting.

But I am in this game to do science and analysis. Not quote it. That’s the point, isn’t it? The citation-collecting racket is for the birds. And I was good at it, too.

But the only real value of a citation is if you need it. If you don’t need it, it’s just more ticky-tacky quasi-intellectual bloatware for the cocktail party circuit. But all everyone want’s is a citation body-count.

Menne wants to calculate the jump as a function of pairwise comparison and then apply an absolute adjustment. The process is Byzantine, to say the least. Whereas I want to bypass the problem entirely– and adroitly: Split the trend at conversion and let the jump, whatever size it is or is not, simply come out in the wash.

In brief, Menne calculates an adjustment using pairwise comparisons and applies it. Can you say “Margin of Error”? Whereas I think I can bypass the entire problem without even a ripple and virtually no MoE at all.

So if I can do this, why cite Menne? Why cite anyone? You-all can cite me if you like.

You think of this as simplistic fumbling. I think of it as elegant simplicity. It’s the age-old conflict between academia and sure-’nuff empirical science. It’s all part of the paper-counting game, where five bad papers carry (more than) five times the prestige of one good paper. And the number-of-times-cited game where you get a point for each citation you have and one point for every time you are cited by someone else.

I don’t play those games. I like my “Just Do the Science” game better. I analyze when I can and cite when I must. Therefore any time I can bring the cite-count down is a victory.

That’s over for now. I did my share in the field, though. I made photographic surveys of a dozen stations. (Garde un chien.) I have put in countless hours capturing the actual stations on satellite images, which, in most cases, determines the rating of the station. And that’s “actual observation” even if there aren’t any actual fields in the immediate vicinity of the work and you can do it in bed. That part never quite stops.

So we I have the tools and I prefer to use them. Not stick on some some pre-made decal of a citation. I only do that when I need to.

Sorry, Evan, but then your earlier response no longer makes sense. If Anthony Watts, according to you, respects the people at NCDC, why did he accuse them of fraudulent conduct?

Or did you perhaps mean to say that he now *does* respect them, after he realised his accusations were pure and unadulterated cow dung?

I am not sure I understand you. When did he do that?

Anthony has never been of the opinion that deliberate fraud was going on in the NOAA. Some of his posters, okay, not so much. But not him and not me.

We have always thought they had been sloppy, a bit careless, and a victim of confirmation bias. And probably understaffed, underfunded, and overwhelmed. That can happen to anyone, especially in areas considered peripheral. We have never thought they were in any way fraudulent.

We don’t think NOAA was or is guilty of any fraud. We don’t think NOAA is guilty of anything. We just think the NOAA is wrong. We think the error was unintentional, even understandable. But we do think they made an error.

That is what I mean. I am sorry I have been unclear on this point. We are not accusing NOAA of deliberately cooking the books or anything else. I was always at odds with Steve Goddard on that point. I just think they unintentionally ran afoul of a statistical artifact. That artifact fit their framework and expectations. So they sort of stopped looking. That’s all.

“…The startling conclusion that we cannot tell whether there was any significant “global warming” at all in the 20th century is based on numerous astonishing examples of manipulation and exaggeration of the true level and rate of “global warming”.

That is to say, leading meteorological institutions in the USA and around the world have so systematically tampered with instrumental temperature data that it cannot be safely said that there has been any significant net “global warming” in the 20th century.” (D’Aleo & Watts, 2010)

“Anthony has never been of the opinion that deliberate fraud was going on in the NOAA. ”

Actually, he was. Kevin already points to the relevant document, although you will have to find the first version, and not the present. in that version the following words were to be found:
“Around 1990, NOAA began weeding out more than three-quarters of the climate measuring stations around the world. They may have been working under the auspices of the World Meteorological Organization (WMO). It can be shown that they systematically and purposefully, country by country, removed higher-latitude, higher-altitude and rural locations, all of which had a tendency to be cooler”.

Pretty threadbare for the money. It says there has been so much tampering with the record and exaggeration the error bars exceed the amount being measured. It does not accuse anyone of fraud. And if it had, then that would be the money quote.

Hullo, Quiet Waters, by the way. If you have anymore question, please ask. I really don’t think that is damning. Or even darning. Not much money in it for a money quote.

I’d like a more reasonable environment so I can get the feedback I need, but no matter where I go, CAGW or skeptic camps, the both do it, all I get is a litany of causae belli concerning half-imagined, half imputed misdemeanors. At least you were also interested in the paper.

From the quotes it is impossible to come to any conclusion other than AW believes that what he calls tampering, data manipulation, and exaggeration was systematic and intentional.

Why would *anyone* choose that title except to impugn the integrity of the scientists involved? Not only that, it’s the coward’s way – by putting a question mark at the end so that AW can always respond: I never said they were deceiving anyone, I just asked the question!

One gets the impression he does not like me and that he is a somewhat emotional chap.

I could not care less about him. Just a source of a huge amount of misinformation on the net on climate change.

If humanity decides not to do more about climate change, so be it. That is democracy and that is an important value. We will cope somehow. It would be a crying shame if the decision was made, however, based on the stream of utter nonsense coming from WUWT. I thus see it as my duty to once in a while point to the fact that WUWT is about the worst source of information on climate change you can imagine.

Things like truth and honesty? For me they are the foundation of our democracy, freedom (human rights) and prosperity. To see them sacrificed for short term political profit is a bad sign of the times.

And that is not just Anthony Watts. It is clear that the WUWT community does not care about being misinformed. Even in the most obvious cases for which no science degree is necessary, such as misquotation or misinformation on the traffic of WUWT. Even in such clear cases no one spoke up and defended the truth. Very rarely this does happen, but then unfortunately with as argument that is give a bad impression, not because honesty is a value in itself.

This gives the impression that truth is not held in high regard at WUWT and is a lot less important as the political cause about which one is not supposed to talk too much. Probably because it is rather unappetising.

Evan Jones: “You are a professional in the field under consideration. So you are held to a higher standard than the normal poster. It may not be site policy, it may not be fair, it may not be democratic and it may not be egalitarian, but it’s reality. … You possess actual power in this context, and therefore you are stuck with an increased standard. Call it an adjustment. Consider it a mild form of flattery.”

There is a word for that: hypocrisy.

That is okay, it saves me from wasting a lot of my precious life time on WUWT. But naturally it is my role to point to this hypocrisy and how it is another sign that WUWT is not about science, not about improving our understanding of the climate system, about being sceptical and judging the arguments of the others, but about politics.

VV: Does anyone know if there were increasingly more calm wind situations in the 1979 – 1998 period in the USA-48?

Evan Jones: “Interesting from a tactical viewpoint, too, as well as from your angle.”

Why only from “my angle”? Weren’t you interested in doing real science? Then you have to look at every angle, not just the ones you like.

Not just study the non-climatic effects that may make the temperature trend stronger, also the ones that make the trend weaker.

Evan Jones: “The best way to find out how CRS and MMTS compare is simply to compare the two.”

Just one transition in one country is not enough to study non-climatic changes this way. It will need to become a large global dataset to have value for science.

The data of the paint project of Anthony Watts would nicely fit into such a database. Surely, everyone would like to know what the effect of the old quickly fading white wash compared to modern Latex paints is. Even if it were a cooling effect.

Evan Jones: “I think we are finally learning our lesson about the importance of good metadata. But that only helps us going forward.”

Isn’t James Hansen of NASA GISS often cited on WUWT? With a quote that sounds as if homogenization is bad, but if you read the full quote carefully actually means hat homogenization using metadata only is bad.

“It follows that a necessary concomitant of discontinuity adjustments is an adequate correction for urban warming. Otherwise, if the discontinuities in the temperature record have a predominance of downward jumps over upward jumps, the adjustments may introduce a false warming, as in Figure 1. This might happen, for example, if it is more common for stations to move from population centers toward the suburbs, rather than vice versa.”

If there is a bias in your metadata (station history), homogenizing your data using metadata only easily produces a bias. For example in case of urbanization, the warming is typically not noted in the metadata, but every relocation to the suburbs is. It is similar for growing vegetation, where the growth in not noted down, but the cutting may be. (In addition, metadata is not perfect as well and normally gives you a potential date, but not the size.)

Thus it is always necessary to also homogenize by comparison with neighbouring stations, to detect and correct all types of non-climatic effects.

Evan Jones: “The idea [of homogenization methods] is to improve the accuracy of all. It is used effectively in the mining industry and in engineering. The problem is that it does so at the sacrifice of the accuracy of each.”

It would be great when after all those years of making such empty claims, WUWT would produce a solid paper on homogenization algorithms and why they fail. Or at least an argument beyond the word “smoothing”.

And no, your current Watts et al. (2012) manuscript is not that paper, it studies one specific non-climatic change. It does not study the (homogenization) algorithms to remove these non-climatic changes.

Evan Jones: “But I don’t do the physics. I can’t do the physics. My role is to perform the data analysis.”

Then you’d better collaborate closely with your “physicist co-author”. Data analysis/statistics without understanding can produce horrible results and is typically not productive.

Evan, what part of “purposefully” do you not understand? As Kevin notes, there may be a slight possibility of plausible deniability, but add the obvious conspiracy nuttery in the beginning of the quote, and it is really hard *not* to draw the conclusion fraud was implied.

Of course, within a few days people ‘peer reviewed’ the paper and found
a) there was no removal of stations, but rather addition of stations prior to 1990 – something described in the scientific literature. You’d think someone so concerned about the surface record would know the literature, but apparently not.
b) the ‘removal’ of those stations didn’t do squat with the trend. If anything, the long-term trend of only the remaining stations was ever so slightly less than those with all stations (but that was so much within the error margin we can safely ignore that). However, Watts’ problems with understanding anomalies are well known, so there’s one explanation he fell for E.M. Smith’s nice pictures.
c) there was no preferential loss of rural stations. In fact, quite the opposite if you’d look at the proportions (the more relevant parameter).

In other words, just about all important conclusions were opposite to what was claimed in that document. And it had the title “policy-driven deception?”

Sorry Evan, I am interested in the paper but many of the statements I see above make me seriously doubt the rigourousness of your research. I have to say that I doubt it’ll ever see the light of day in the serious scientific literature – though I could quite easily believe that it will be published somewhere online with great fanfare and attention from certain sectors of the MSM.

I do have a question though – I may have seen this asked before but can’t remember where or recall an answer:

Were you aware of the (homogenised) trend of a station before site classification was made? Not necessarily the exact trend but whether a station was, lets say, running hot or cold?

Were you aware of the (homogenised) trend of a station before site classification was made? Not necessarily the exact trend but whether a station was, lets say, running hot or cold?

Not at first. One wants to do the thing blind. I’ve reviewed them a lot since then, though. My work on that will be available for scrutiny, along with everything else. You will be able to review the ratings.

Question that might be obvious to those talking but don’t we have temperature sensors on buoys over ocean water? And temperature sensors in the water? It seems that would be an interesting place to look for jumps, trends and whatever Tobs=T-dT when there is a nice, homogenous heat sink over long periods of time. Humidity might be an ugly thing to remove but seeing trends in how the heat sink varies over temperature in addition to gradient would at least be useful in understanding more complicated siting like cities. I would presume these sensors have similar homogenization, jumps, trends. etc that are present in land based instruments but not have nearly as much siting issues. Is that not correct?

It’s not semantics. It’s about doing it yourself instead of using someone else’s work.

I’d much rather do something based on what Victor suggested: Calculate the 5-year mean before and after the conversion to scope the jump. Then do the same for all the other stations of the same type (i.e., in or out of compliance) in the region, and subtract the second from the first and add the result to all datapoints after the conversion.

Besides, when Menne was doing pairwise, he wasn’t doing it Class 1\2 to Class 1\2, he was doing it any class to any class.

So I think I’d prefer to do it myself.

Was classification altered when trend was known & if so why?

Sure. I had to review them many times as better resources became available, esp. Google Earth improving over the years. All my work on this will be online and available for review, with instructions on how to go about it yourself.

The spreadsheets will also be archived so you can re-rate and re-run all you please.

Another point, too. MMTS created a downward jump for Tmax. But it creates an upward jump in Tmin (Menne 2009.

Basing the adjustment by class grouping will create some very interesting results. All a result of this extended exchange, I might add. Every time I go to a blog where the premise is challenged I get something out of it. Q.E.D.

This is better. First of all, it is a conference. A highly directed one. Second of all, it’s a written record of the discussion. Third of all, the agenda can shift as needed. I did not know coming into this that I would be calibrating step jumps coming out of it.

But why would we want to use Menne unless we had to? He is doing pairwise comparisons of class 1\2 stations with stations of all classes.

That’s a reflection of the exact same thing that has screwed homogenization: If there is a trend bias on account of siting, you can’t do a pairwise between well and poorly sited stations. The comparison has to be well-to-well and poor-to-poor .

That is part of the reason why poorly sited stations get adjusted to almost as warm as the poorly sited stations. A statistical artifact.

I don’t know what my results will be, but my hypothesis is that the poorly sited stations will show a larger jumps than the well sited stations. After all, they are warming faster.

I’m also not sure if they are all going to be warming jumps. There is a low jump for Tmax, but a high jump for Tmin. So the jump could be in either direction, depending on local conditions. So could be strong or weak jumps in either direction.

Menne’s findings, for purposed of context, were that for the 30-year period 1980 – 2009 (almost the same as our 1979 – 2008 study period), Tmin was adjusted +0.11C/decade, Tmin is adjusted -.07C/d (with an overall +0.02C/d on Tmean).

At a conference you would get feedback from people who are actually knowledgeable about what happened with the American network in your period of interest.

Now you only get feedback from one person who understands homogenization methods a little and from people who understand science in general. And the algorithms to remove non-climatic changes (homogenization) (where I am knowledgeable) is another topic as the reasons for non-climatic changes (in a specific country) (where I am not knowledgeable).

At a conference you would get more people that would tell you that you have not provided any proof whatsoever that the differences in the trends you found are due to gradual non-climatic changes, rather than due to abrupt ones.

At a conferences you would get more people that would tell you that you only studied a non-climatic change in the USA and that you are well advised not to draw conclusions about homogenization method from that, for the simple reason that you did not study these methods.

Thus maybe at a conference you would get so many people pointing to similar problems that you would take these ideas seriously and would do better work and communicate its implications more fairly.

But look what we have covered right here. You have convinced me to tackle MMTS adjustment, using much the same methods that Menne used. With one substantial difference: the comparisons are made regionally against other stations of the same ratings groups (1/2, compliant vs. 3\4\5, non-compliant.

Your explanations have helped me to see what Menne was doing incorrectly: he was failing to account for microsite during his pairwise comparisons. So some of the increase that is the result of bad microsite winds up attributed to those with good siting.

In other words, it is exacerbating the homogenization issue — in much the same manner that final homogenization does. As you say, homogenization bombs out if there is a gradual trend bias present in the sample. The rest follows.

I am using the method you recommended earlier this thread: do 5-year baselines before and after the equipment conversion of compliant (or non-compliant) stations. Compare the difference with other the compliant (or non-compliant) stations in the same region over the same period. Subtract the former from the latter (be either a positive or negative number), and there is your adjustment. Apply that to adjustment (which could be a plus or minus) to all months after conversion.

I.e., using Menne’s basic method, but taking microsite into account while doing so. (Menne does not acknowledge microsite bias except as a step change.) I think you can figure out the implications of this.

I think you missed V^2’s point. Holding your actual work up to deep examination and critique by many experts at a conference does not equal mild discussions of ideas on a blog, regardless of how well-intentioned the commenters may be.

But don’t discount the blog feedback. Call it “Post-Modern Independent Review”. In this format, I can depart for some time and think over what I have (or have not) accepted or rejected.

The microsite bias seems to run through all of the procedures that require any sort of pairwise (and, of course, regional) comparisons. Even MMTS-adjustment.

Therefore you either have to bin the station set by microsite (into at least the two main subcategories: compliant vs. non-compliant) or your results will be incorrect. Not so much in the overall station mix, but among the subgroups, themselves.

This is not the sort of thing I can get at a conference, with the questions bouncing in from all sides (if one can even manage to elicit the questions in the first place).

This way I get to bite off what I (think I) can chew. Then chew.

Current subject being MMTS conversion: Let’s take a look at these jumps and see how jumpy they really are. And this time we compare apples with apples and oranges with oranges, which is Menne et alia’s “missing step”.

At a conferences you would get more people that would tell you that you only studied a non-climatic change in the USA and that you are well advised not to draw conclusions about homogenization method from that, for the simple reason that you did not study these methods.

If you consider, it’s very close to what I am doing now. It’s true that I am not doing the recursive logic (Watchers watching Watchers watching Watchers). But it is at least binning for siting. And that’s one thing all those experts never did. Otherwise it is not unlike Menne’s.

You said at one point that you would curious how Class 1\2s would homogenize against each other. Well, what I’m doing is a first step towards that.

Dr. Connolley put my feet to the fire over on JoNova, He makes a legitimate point: My objection to homogenization is not so much against the h-word, itself, but to its application in the specific case of temperature measurement. Well, yes, but it goes deeper than that.

It can’t be avoided in some cases (such as MMTS adjustment). But it is fire. I’ve done it and it is playing with fire. I am doing it now. It carries hideous inherent potential artifacts (stuff you can’t even see) and must be used only when absolutely necessary.

I think using homogenization and related methods to infer metadata is far more hit and miss than is acceptable. That AP over on the JoNova site, for example. Turns out that station not only didn’t move (if that last guy is telling it to us straight), but it’s a Class 3 station (using Leroy, either 2010 or 1999), with a warm bias from the paved area to the north. I.e., a poor location. That makes it unlikely that the jump was a result of an unrecorded move to a better location. Yet that is what BoM had inferred.

If metadata is the problem, I think it is more productive to find it by research (as was done in the above case) than infer it by pairwise comparison.

Thus like Marco, I had assumed that your hypothesis was that the heat sink is doing the warming during the 1990s to make the temperatures hotter and hotter.

That is correct. The trend was exaggerated. Something to remember if it starts cooling significantly.

1. Time of observation bias stronger in rural stations.

We drop all stations with TOBS flips.

2. More problems due to the UHI in the bad stations.

I honestly don’t find a lot of difference between rural and urban trends for well sited stations. The offsets are higher, sure, but the trends are much the same.

3. Selection bias (bad/good stations at the end of the period may have been better/worse before)

Any case where a rating changed during the study period, we drop that station. We do our best (GE gives a good look going back to the early 1990s, and better resources will emerge). In cases where there are no moves, siting either stays the same or gets worse. Never better.

So one or two unmoved Class 3s (or even 4s) that were Class 2s may have slipped by (in spite of our efforts), but there won’t be any unmoved Class 2s that used to be Class 3\4. Any error of that kind will tend to spuriously bias against our hypothesis, not for it.

“I believe we will be able to demonstrate that some of the global warming increase is not from CO2 but from localized changes in the temperature-measurement environment.”

Anthony Watts
June 17 2007 ( after about 2% of data collected)
Interview with Pittsburgh Tribune.

“It gets worse. We observed that changes in the technology of temperature stations over time also has caused them to report a false warming trend. We found major gaps in the data record that were filled in with data from nearby sites, a practice that propagates and compounds errors. We found that adjustments to the data by both NOAA and another government agency, NASA, cause recent temperatures to look even higher.

The conclusion is inescapable: The U.S. temperature record is unreliable.”

Is the U.S. Temperature Record Reliable?
Anthony Watts
Publisher: Heartland Institute.
Peer-reviewed? No.

“Instrumental temperature data for the pre-satellite era (1850-1980) have been so widely, systematically, and uni-directionally tampered with that it cannot be credibly asserted there has been any significant “global warming” in the 20th century.”

“The Earth is warmer than it was 100-150 years ago. But that was never in contention – it is a straw man argument. The magnitude and causes are what skeptics question.”

WUWT.
Anthony Watts
Oct 21, 2011 (Post-BEST)

One hopes Evan is less willing to distort data than he is languauge if he honestly believes that an accusing a body of uni-directional tampering, manipulation and exaggeration is not a serious allegation of malpractice (How does one tamper ‘unintentionally’?). Anyone acting in good faith, with a scrap of honour, anyone who wanted to be taken seriously as an objective researcher would retract the Heartland and SPPI ‘reports’ and apologise once the allegations were shown to be baseless.

To the reality-based community, anyone with a history such as Watts’, littered with baseless allegations, not a little vitriol, and a clear-as-crystalagenda and bias, and 180-degree turnarounds has a mountain to climb to rebuild his credibility and before anything he writes will be taken without a shovelful of scepticism.

“… it cannot be credibly asserted there has been any significant “global warming” in the 20th century.”

Of course this is nonsense. We don’t need a single thermometer reading to know there was significant global warming in the 20th century. Anyone thinking they’re going to prove otherwise is on a snark hunt.

They’ll have to reconcile “no significant warming” with mounds of phenological records that don’t depend on any temperature records. Like most pseudoskeptic theories it’s a one-trick pony, unable to deal with multiple lines of evidence.

“The Earth is warmer than it was 100-150 years ago. But that was never in contention – it is a straw man argument. The magnitude and causes are what skeptics question.”

Not sure what is so terrible with that statement or why anyone on either side of the argument would object to it.

As for me, my position is that I am a lukewarmer and ascribe much of the warming we have seen to CO2 (with all the raw forcing, but missing the projected net feedback).

And, yes, nearly all the adjustments have increased trend. I can see that being a legit effect of TOBS, at least in the US, but we are also seeing it as an effect of inferring TOBS.

Furthermore, when you use the necessary recursive logic, you are adjusting based in your first cut of adjustment. I’ll bet that when that AP out in Australia on JoNova is used for adjustment of neighboring stations, they use the adjusted,/i> version of that station, not the raw version.

In any case, my main purpose here is not defending Anthony. Or attacking him, either. I am interested in improving the paper. And even if I did started attacking Anthony, I would certainly not stop with him, and you can count on that.

We have learned much since 2009. At first we all believed what Menne believes: that only an actual change in microsite could bring about change in trend, What we have found, however, is that bad microsite exaggerates trend even if it is unchanging.

The conclusion is inescapable: The U.S. temperature record is unreliable.

Good luck escaping from that one.

But the VeeV is drawing me in closer and closer to the open flame of homogenization. I won’t be able to escape at least one additional iteration in my MMTS-conversion work because there is a disgusting logical imperative that forces my hand.

“C’mon. Just one more iteration . . . “, whispers Old Scratch from his perch atop my left shoulder. The trick is to catch oneself and correct. When one fails to do so, someone else catches you. Happens all the time in science.

One hopes Evan is less willing to distort data than he is languauge if he honestly believes that an accusing a body of uni-directional tampering, manipulation and exaggeration is not a serious allegation of malpractice

They could have done a better job, shall we say. TOBS and the move to airports appear to me (so far) to have been legit reasons to cool the past or warm the present. They should have stopped there.

But inferring TOBS and moves from pairwise comparisons will lead to blatant (and important) errors like at Amberley. Not only was the station not moved, but it was Class 3, making it unlikely the trend would have been decreased even if there had been a move. If anything, there will be a warm jump for when the area to the north was paved, and exaggerated warming trend as well (it’s Class 3).

And it’s a -0.1C/d to +0.25C/d shift over a 60-year span. Much greater effect on a 30-year span. That’s on the very high end of adjustments I am currently doing, and I am cutting crude.

They don’t seem to be much thought given to looking at the outliers with missing metadata and then picking up the phone and calling those places and attempting to find out as much about the missing metadata as they can. I bet they would turn up quite a bit. But their first resort is to homogenize.

In other words, the results of your work will yield nothing other than an unreliable trend based on an unreliable temperature record?

In other words, you can’t escape the fact that the US Temperature Record appears to be unreliable, after all.

Fortunately, there are ~80 well distributed Class 1\2 stations without moves or TOBS issues (out of 1200+), so we can compare those trends with the trends of poorly sited stations and the adjusted data.

BoM inferred there was a move by making pairwise data comparisons. The trouble with this approach is what the fortuneteller told the cop.

and how do you know it wasn’t moved?

I have spoken with several ex RAAF guys who were based at Amberley in the late 60′s and early 70′s. They clearly remember the stevenson screen being near the back gate at the end of Old Toowoomba Road. Pretty much exactly where it is now.

This is, of course, stipulating that what the poster is telling us is accurate.

For interest coord lat -27.633210 deg long 152.712626 deg

And there she is, sure enough. Looks like a CRS and an F-P or equivalent. Class 3. Not good microsite, especially considering it’s an airport.

So even if there were a move, it would far more likely be a warm bias than cool bias. In fact, when the area was paved, that turned it from a class 1 to a Class 3. So I do not see any cooling bias here, only potential warming bias.

Bear in mind that the location of the Amberly CRS was not even known to the posters there. I failed to find it, my only credit being that I thought the nearest likely-looking object was not the CRS (it wasn’t). So the poster found it dead on, based on interviews with contemporary observers. (That makes him a site surveyor — and all site surveyors will always be my brothers..)

This adds credibility to his claim. Also, observer testimony carries more weight than metadata unless there is reason to question it.

“Fortunately, there are ~80 well distributed Class 1\2 stations without moves or TOBS issues (out of 1200+), so we can compare those trends with the trends of poorly sited stations and the adjusted data.”

I wonder how many ways you can slice 80 stations out of that set of ~1200 and get the results you believe must be correct?

Evan, there are so many other lines of evidence that point to the same conclusion that while you may come up with a ‘clean’ record, it’s almost inevitable that to be correct it will agree with the existing data.

For instance: “Freeze and breakup dates of ice on lakes and rivers provide consistent evidence of later freezing and earlier breakup around the Northern Hemisphere from 1846 to 1995. Over these 150 years, changes in freeze dates averaged 5.8 days per 100 years later, and changes in breakup dates averaged 6.5 days per 100 years earlier; these translate to increasing air temperatures of about 1.2ºC per 100 years. Interannual variability in both freeze and breakup dates has increased since 1950. A few longer time series reveal reduced ice cover (a warming trend) beginning as early as the 16th century, with increasing rates of change after about 1850.”Historical Trends in Lake and River Ice Cover in the Northern Hemisphere, Magnuson et al, Science VOL 289 8 September 2000.

There are dozens, if not hundreds, of these phenological studies that all say the same thing. A temperature reconstruction that disagreed would immediately be an outlier and suspect – not because of who made it or how it was done, but because it would be in disagreement with too many other lines of evidence. That’s why I say you’re on a snark hunt.

So, it’s not a question of how we classify a station, or whether it moved, changed instrumentation, changed TOBS, or any other confounding factors – the results are constrained by other lines of evidence.

This is why someone that comes in from a purely statistical POV has little chance of convincing climate scientists their data is in gross error – it can’t be. If it was in gross error it wouldn’t be in agreement with all these other lines of evidence.

But I am not disputing AGW. I agree that it has warmed. I agree that man has most likely been the main cause. But the recent warming has been exaggerated by poor siting. And if it starts to cool, which it may, that cooling will be exaggerated as well.

This is why someone that comes in from a purely statistical POV has little chance of convincing climate scientists their data is in gross error – it can’t be. If it was in gross error it wouldn’t be in agreement with all these other lines of evidence.

It can be when a trend-amplifying bias is pervasive throughout the surface station record. So it wouldn’t show up. And in the homogenized data there would not be a trace left behind. That’s the point. Microsite bias permeates every step of every pairwise adjustment process.

We will, of course, be hitting it from the physics angle, as well. So it won’t be a statistics-only study. It will be backed by a mechanism that explains why and how (and to what extent) this occurs.

the results are constrained by other lines of evidence.

Indeed they are. Right here on Stoat, it was pointed out that our results did not coincide with our own co-author’s (Christy) UAH results. I pointed out that Dr. Christy had calculated that LT trends should exceed surface trends. The reply was, yes, but only ~20%, and we were nearly 45% under.

Our current results (a bit higher than our 2012 results) split the uprights at ~25% lower than UAH and even closer to RSS.

So Dr. Christy’s hypothesis is supported, and it falls into place and into the overall context. We are not claiming there was no warming. We are saying that bad microsite biases trend, whether it be a warming or a cooling trend. The evidence is very strong.

““The Earth is warmer than it was 100-150 years ago. But that was never in contention – it is a straw man argument. The magnitude and causes are what skeptics question.”

Evan – Not sure what is so terrible with that statement or why anyone on either side of the argument would object to it.”

C’mon Evan – you’re not that dumb. The point was that Watts was saying the exact opposite just one year earlier, saying warming very much WAS in contention and so here is trying to rewrite history, to put it kindly. Lying, to put it honestly. One year he says there is no credible evidence of global warming, the next he’s saying there is and he never said otherwise. Even you can spot the credibility gap. Or maybe not.

And you might want to check the dictionary before you confess to ‘tampering’.

You rely on double hearsay. An anonymous commenter “Paul” claims to have spoken to unidentified people, supposedly ex-RAAF, who all remember from more than 40 years ago exactly where the station was located. I’d be a little bit more skeptical about that.

But even if it wasn’t moved, people all over agree there is a clear discrepancy around 1980 for the minimum temperatures. Nick Stokes pointed it out, Bill Johnstone has noted it, and of course the BoM itself.

Regarding asking for giving credit for Fall et al….sorry, not from me. I can’t see how you could *not* publish this, after Menne et al had already shot big holes in the invective from Watts. At least you guys could still claim “look, there, really some issues, even though it does not change the trend of the average”.

And if it starts to cool, which it may, that cooling will be exaggerated as well.

Are you saying that if it starts to cool, people will tear up the pavement on the runways, re-site the stations to cooler places, and so on?

Evan, you’re doing something useful (or at least potentially useful) with your all your work on analysis of stations.

But to put it bluntly, your arguments regarding “heat sinks” and the like just don’t make any sense from a physical perspective. I think you’d be much better off if you presented it as “here’s an analysis of the stations, the statistics say something odd is going on, we think it’s worth looking into this in more detail.”

“And if it starts to cool, which it may, that cooling will be exaggerated as well.”

There’s your denial right there.

Oh, come off it. We are in a pause because of PDO pushing down and CO2 pushing up. If the PDO pushes down a little more or some unknown factor asserts, we could easily hit a patch of cooling before the next positive PDO rolls around.

And during the cooling interval from 1998 to 2008 (high start, low end points), the poorly sited stations cooled faster than the well sited.

Are you saying that if it starts to cool, people will tear up the pavement on the runways, re-site the stations to cooler places, and so on?

No, I am saying that the effect that builds up during a warming phase reverses itself and revert during a cooling phase, resulting the the opposite effect. Or to put it more succinctly, what goes up must come down.

Bad microsite exaggerates whatever trend there is out there, either warming or cooling. It is not just a warming bias.

Just came across a new paper by our Greek colleagues. On average the homogenization in Greece decreases the trends. If that were the case globally our climate “sceptics” would love homogenization.

But the trends did increase in Karpathos, the Siteia and the Kalamata. That will be the next Amberly’s for some Greek Murdoch tabloid. Sorry for having been made so cynical.

A first visual comparison between raw and homogenized seasonal trends reveals that the trend is reduced in all seasons. The most pronounced differences are found in summer where strong positive trends were found before homogenization in northern Greece (climatic region A) with values 1.2 °C/decade for the Xanhti station, 1.0°C/decade for the Soufli station and 0.9 °C/decade for the Drama station and also in the eastern Aegean (climatic region F) with value of 1.1°C/decade for the Samos station. After homogenization these trends decreased by 0.9°C/decade for the Xanhti station and by about 0.6–0.7°C/decade for Soufli, Drama and Samos stations. Also the stations of Argostoli, Pyrgos, Serres, Piraeus and Aktio present a major change in summer temperature trends with a decrease of about 0.4 – 0.5°C/decade after homogenization. Another main difference between trends is that before homogenization negative trends were found in the southern Aegean and Crete (climatic region G) and in western Greece (climatic region C), with values −0.5, −0.2 and −0.1 °C/decade for the Karpathos, the Siteia and the Kalamata stations respectively, while a warming trend in summer is confirmed for all stations after homogenizing with HOMER.

Evan Jones: “Bad microsite exaggerates whatever trend there is out there, either warming or cooling. It is not just a warming bias.”

And somehow only does so on decadal time scales? Why doesn’t your magical mechanism exaggerate the differences from your to year (or from month to month, from day to day, from hour to hour)? We know from how closely the pristine US reference network (USRCN) tracks the normal US historical climate network (USHCN) that the year to year changes fit very well, on a year to year basis the trends are somehow not “exaggerated”.

I would second Raymond Arritt, why don’t you say: “here’s an analysis of the stations, the statistics say something odd is going on, we think it’s worth looking into this in more detail.” I might even add: we haven’t got a clue, it seems to be physically impossible, please help.

Evan, please explain by what magic Amberley shows a *very* strong cooling trend for Tmin, whereas just about no other station in its vicinity shows even a cooling trend.

The evidence BoM provides is that Amberley is completely out-of-whack with surrounding stations, and that it is even possible to point at a rather specific point of divergence. It did not claim the cooling was evidence.

On average the homogenization in Greece decreases the trends. If that were the case globally our climate “sceptics” would love homogenization.

It certainly could have done. I don’t know the situation with the stations there. In the US, it increases the average from 0.278 to 3.20 (using our older USHCN2.0 figures). But it could either increase or decrease the average depending on the dataset.

But the point is that the unhomogenized average itself is wrong anyway because it combines good and poorly sited stations. Homogenization will bump the trend a little in either direction, but what I object to is that it adjusts the not-so-outliers to match the others.

That means when you look at the adjusted data, you cannot see those interesting non-conformists, so you never perceive there was an issue with the data in the first place.

After homogenization these trends decreased by 0.9°C/decade for the Xanhti station and by about 0.6–0.7°C/decade for Soufli, Drama and Samos stations.

Those are very large adjustments.

Sorry for having been made so cynical.

We have all become cynical over time and in the trenches.
Rise above it as I know you can (and I have seen you do so).

If you and Anthony could somehow set aside your differences it would be telling.

I was serious when I said your (deserved) high standing in this field places a stronger obligation on you than on most others. It may not be fair, but it is what it is, and maybe it should be, at that. Remember what the Sipder-Man said.

We are men of the world and we are here to discuss a serious scientific issue. A lot more actual science gets done if opposing sides are on speaking terms with each other.

Evan, please explain by what magic Amberley shows a *very* strong cooling trend for Tmin, whereas just about no other station in its vicinity shows even a cooling trend.

IIRC, some of them do, but they are adjusted as well. There was also the question of which stations were used for that. Heat shifts around. If it warms rapidly in one location it may well be simply because things shifted.

You think that the data alone is enough to infer a move even if there is definite indication that there was no move and the metadata does not show a move. Even if a move is unlikely to have caused such a change (the station currently being Class 3)..

I do not. I don’t think the data alone is ever a reason for pairwise adjustment. If it is extremely out of whack, then it is a definite outlier and you drop it (HCN notes such data). But otherwise, there must be some sort of indication of a problem with the instrument, a move, a conversion — something.

As it is, we have better evidence than we usually do that the station did not move.

And you might want to check the dictionary before you confess to ‘tampering’.

I am under no illusions about my own bias. I have to suppress it, continually. I don’t believe others when they say they do not experience this. When I reviewed with better resources some nice cool-trend stations I had pegged as class 2, I had to revise them to Class 3. Why didn’t I get it right at first? So for every tamper there must be an untamper — trying to wear the hat of your worst critic..

We know from how closely the pristine US reference network (USRCN) tracks the normal US historical climate network (USHCN) that the year to year changes fit very well, on a year to year basis the trends are somehow not “exaggerated”.

It supports rather than refutes the Delta Sink argument: The reason there is no diversion is that there is a flat trend over that period. Bad microsite does not create a trend. It only amplifies a trend. On a yearly basis, the diversion would be very small, smaller than the MoE. But it builds up over time if there is warming (or cooling) over time.

We are talking a scale of only ~0.15 per decade. MoE will bump that up/down on a year-to-year basis, but it will build up over time. And MoE for an individual station will, of course, be larger than a larger number of stations.

What you think should be adjusted by homogenization, I think should be done by oversampling and dropping (if necessary). Adjustments to be applied, but with as light a touch as possible.

Beware. Homogenization leads to very satisfying results and a smaller (deceptively external) error bar. So it is a comfort zone and a trap. It’s a tool, but I think it is a more dangerous one than you think.

To me the homogenization methodic is a limited last resort (as per MMTS adjustment) when there is no alternative.

But to others, it’s a first resort, as was demonstrated by the Amberly example. And it is not impossible that BoM is wrong, even with what we currently know. It is just that the BoM simply presumed and forged ahead when they could have made a phone call or three.

I will adduce the possibility that the observed changes are a result of greater natural sensitivity to climate change. A pro-action argument. It then becomes an issue of what those changes are, how pervasive and general, and whether they are linear or something else.

I think it might be interesting to examine extinction patterns (especially among the lesser subspecies) during the ice age(s) and speciation at the onset of interglacials. That would give us a ~10C swing to examine and compare.

Understand, though, that it is important to get the temperature scale right. Whatever follows, whichever side of the argument it affects, what is, is. And we really do need to know what is is.

I know our paper will be interpreted by some to indicate AGW is a myth. I will dispute this flatly whenever I see it.

However, a not-so-quick scan of Class 1\2 stations vs. Class 3\4\5 shows a highly significant difference between the two.

Leroy does count a structure or paved driveway as a heat sink. He does not, however, classify soil as a heat sink. He finds an offset difference (acknowledged by NOAA), though he does not address trend. If soil had a similar effect as structures or pavement, Leroy would not have found an offset difference (nor would have Yilmaz).

Yet the adjustment procedure places the final result quite close to that of the poorly sited stations — and higher than the average of all stations.

Exactly, the soil has enough heat capacity to influence the temperature on decadal time scales. The heat capacity of all the other “heat sinks” (buildings, pavement, etc.) are much too small and will not influence the temperature above time scales of a season.

1. But in that case the influence would still be stronger on small temporal scales and we do not see any problem in the year to year variability.
2. And the soil would dampen, not amplify the variability (the soil would take up more heat in above average hot times and give of heat on below average hot times).
3. And the surface heat flux is much too small to influence the (radiatively protected) temperature sensor.

Evan Jones, I can understand that you hold special opinions on topics you are not knowledgeable about. Thus in the previous discussion at HotWhopper, I have ignored such statements. However, you should know enough about homogenization by now that your defense of the nonsense The Australian is sprouting about Amberly seriously calls into doubt your scientific intentions. If the difference signal between Amberly and several of its nearest neighbors show a jump, it is clear that something happened at Amberly that is not representative for the regional climate of these stations. You have now worked with the data, you have made such corrections yourself and should understand exactly what happens. Others can act as if they are ignorant and many of these people make great effort to stay ignorant. You can no longer do so.

It is very easy to lie with statistics. Especially in your case where you removed so much of the data, made your own classification, selected a special period, etc. Taking such a statistical result without physical mechanism that could explain it seriously takes trust. Your defense of The Australian destroys a lot of trust. Do you want to do science or politics?

You could be a hero, Victor. When we publish, we will release all of the station ratings (with the metadata and physical evidence to back them up for all to examine). So go to your meetings and tell them to do their procedures, but with one difference, one that you even suggested to me over at Sou’s place — it’s your own idea:

Tell them to bin for microsite. Have them homogenize only the Class 1\2s against other 1\2s and do the 3\4\5s separately. I predict you will show that the fault here lies not primarily with homogenization, but with a trend bias on NOAA’s end. You could be the one who saved the climate record. Be part of the solution.

You want the “interesting” you mentioned earlier? Now that would be interesting. I would like to be a fly on the wall in such a conference.

You have now worked with the data, you have made such corrections yourself and should understand exactly what happens.

All too well.

I am basing the corrections I make based on solid, complete metadata. NOAA is excellent about dates of conversion, and I only found one case where it was wrong. (“Old Griz” Huth up at Mohonk Lake contemptuously rejected the MMTS after they hauled it up there, but it’s still on the books. I wonder if they adjust for conversion?)

And let me tell you that when I do adjust, there are quite a number of cases where you would expect the exact opposite effect. A cool Class 4 that converted in 1986. That’ll be a nice warming correction, right? Not so fast, kimosabe. One would never have made such adjustments if it were not for the metadata (which, as I said, is quite complete).

When you are shooting blind with no metadata, all of that just gets smoothed over. None of those “inconvenient adjustments” that are so counterintuitive as to what you “know”.

If the metadata is right and you can’t do homogenization based on metadata, you most surely cannot do it based on no metadata. But it’s so nice and easy — and no inhomogenous metadata to mess things up. It’s a honey trap, I’m telling you.

True. But doing so worked against out hypothesis, not for it. Also, we did it on the urging of the independent review we received and in the manner prescribed.

We will provide a complete inventory of all dropped stations and the reason each one was dropped.

made your own classification

Again, yes. Those will be available when we publish so that you can review them.

selected a special period

We selected the 1979 – 2008 period for Fall et al., which gave us different results. So that was selected beforehand. It is an ideal period for a number of reasons: It comprises a period of unequivocal warming (roughly corresponding to the late positive PDO). Metadata is excellent for that period, and there are still a sufficient number of stations for our smple after having dropped for moves and TOBS.

Taking such a statistical result without physical mechanism that could explain it seriously takes trust.

I am not asking you to trust me. I am asking that you review the data and the parameters. And check the ratings, should you care to. When the paper is out, all supporting material will be archived.

Your defense of The Australian destroys a lot of trust. Do you want to do science or politics?

I have not commented one way or another on either the Australian or on politics.

I observe only that BoM’s correction does not correspond with either the metadata or the surveyor interview, and that the location of he station carries with it a warm bias. It is based on pairwise comparison alone.

As for me, I think heat and lack of it move around. If it gets hotter here, it may well be precisely because it got cooler in the next neck of the woods. Homogenization runs afoul of that one — bigtime.

The Rev may be beaten, again and again and again, but he keeps standing up for more. It seems that revenge against the nasty boffins he had to suffer under those long years ago will remain his Holy Grail until the end of his life. I predict a considerable added amount of bitterness between now and then.

Evan, this sounds suspiciously similar to the results you’re seeing. Highs are higher and lows are lower. This is the result of a temperature-dependent systemic bias inherent to the MMTS sensor. The MMTS Bias I adjustment was designed to reduce this bias. Are you applying a temperature-dependent correction – or just an offset?

Are you applying a temperature-dependent correction – or just an offset?

A temperature-dependent correction using regional comparison (recursive). 7-year intervals (or as much as the interval allows). Which the sideswipe of which is s crude homogenization of a baker’s third of our dataset, I might add. (The horror.)

An offset would work fine for the overall set (applied to Tmax, Tmin, Tmean, as per Menne). I know the bias is said to be worse in rural areas, but the rural stations have worse microsite, on average, than the urban sites.

If we resort to that, I will at least take into account when conversion occurred. But it is essentially averaged pap. individual MMTS adjustments jump all over the place, plus or minus, and not always in the direction expected, although it averages about the same as Menne for Tmean (so far). If results hold up for Tmax and Tmin, we will have tested Menne in a vague sort of way.

So I prefer to do the job myself. As I am going to adjust, I would at just as soon avoid spooning it on. I will do that only if I have to.

According to Menne, who, like us, are looking at this through the lens of a 30-year trend (his is 1980-2009, ours is 1979-2008), the adjustment averages: Tmean: +0.02C/decade, Tmax: -0.07 C/d, Tmin: +0.11C/d to the converted stations, with and overall +0.0139 effect to the entirety of USHCN Tmean.

If you apply that to each of our MMTS stations (carefully acounting for time of conversion, etc.), you get the results I stated earlier.

That was good of you. But when it comes down to it, I am just a citizen playing scientist on TV.

As for the Rev, you haven’t suffered under nasty boffins until you have suffered under nasty boffins the way he has suffered under nasty boffins. The only difference these days is that now it is angry, nasty boffins. The only thing surprising is that he has any bounceback left.

Anthony made the discovery, building on Dr. Pileke’s work in 1995. We have been working on it ever since. I am pleased to be a footsoldier in this effort.

Evan, I’m not clear from your response if we’re talking the same adjustment. Per Hubbard & Lin 2004, if the MMTS reads 20ºC it requires a different adjustment than if it is reading 22ºC or 10ºC or -10ºC. The adjustment is temperature dependent.

The systemic instrument bias is shown in their Fig. 2 and the MMTS Bias I adjustment is given in their Eq. 1 as:
MMTS Bias I = a + bT + cT ^2 + dT^3 + eT ^4
The polynomial coefficient values they derived are in the text.

This is independent of siting or homogenization. It’s simply inherent to the MMTS sensor (Dale/Vishay 1140 thermistor).

That’s one reason why I would rather do it myself. I’ll let the data tell the story, here, unless my final results are an outlier.

Although what you describe could be done, I suppose. I have the data both anomalized and raw. But that is still applying monocrome, just one less layer.

All very intuitive.

The problem arises that with a Tmin and Tmax bias in opposite directions, you get all sorts of offsets when combined with natural conditions . Big ones. Small ones. Positive ones. Negative ones. So even if adjusted for absolute temperature, it would still just vanilla ice cream spooned on. All the individual variety would be lost.

Evan, it’s a known instrument bias independent of siting. It also could explain the non-physical response you describe. It should be applied to all MMTS raw temperatures. There’s also the MMTS II Bias – though I can’t see that affecting the high/low accentuated trends you describe.

I don’t see what makes you so certain that every station will react the same. If temperature makes a difference, other things may as well. I just want to see what the data says.

I am on the same philosophical plane as Mosh or the VeeV: The answer is in the data.

The trick, however, is in asking the right questions. I think Mosh is barking up the wrong tree and I think the VeeV straying into some very treacherous, territory (which I’m still trying to get off my boots — homogenization: the fungus among us.)

Besides, I haven’t applied the recursive (just one turn will be more than enough), and until I see that I see little.

If we need to, we will probably use Menne (2009). J-NG says he has solutions, too. So we’ll see. But it would be so nice to craft this part ourselves. Like I said before, Menne is one citation I’d like to lose.

I am familiar with instrumentation though – including LIGs, thermistors, TCs, and virtually anything else used to measure some physical, dimensional, or electronic parameter.

Many instruments that belong to the same Mfg/Model have a ‘typical’ response curve. This is understandable since they’re usually coming from one manufacturing process, built using the same components, and with the same or similar equipment and operators. They are also tested in the factory using the same chain of traceability.

If Hubbard & Lin 2004 are correct, then the thermistors used in the MMTS stations exhibit a response curve that makes the highs too high and the lows too low. This was based on a direct comparison of the instruments themselves – not comparing stations or searching for breakpoints.

If nothing else it should be run for comparison to see if the results yield anything interesting.

Evan, as a side note, when a company has several different manufacturing facilities that all produce the same instrument it is often possible to tell where an instrument was made simply by looking at the calibration results. I.e., each facility will have its own response curve.

So while I’m *not* sure Hubbard & Lin are correct, it’s a plausible result and helps explain your results which lack a physical basis.

I am bothered that Hubbard & Lin never mentioned their 2004 results in their 2006 paper.

Interesting. According to Menne, it’s the lows that are too high and the highs that are too low.

It’s hard to tell with Tmean. Menne found only +0.02/decade over a 30-year trend. I am finding very little difference at all so far. This is, of course, easily within the MoE (which i have not calculated).

But when I run ^Tmax and Tmin, the differences will pop out in an instant, so I will just wait and see.

Evan, “Interesting. According to Menne, it’s the lows that are too high and the highs that are too low.”

Yes, on the face of it this does seem contradictory – but I’m not sure it is.Hubbard still saw a negative offset, but the response curve wasn’t flat. So the adjustment at higher temperatures is smaller (0.22 @ 26ºC) than the adjustment at lower temperatures (0.42 @ -9ºC). Between these points the response is linear, but outside of them the adjustment moves in the opposite direction.

As I said, this is a temperature dependent adjustment, so the net effect actually depends on the distribution of MMTS station absolute raw values.

Susan, I don’t believe *anyone* is making the MMTS Bias I adjustment spelled out by Hubbard & Lin 2004. Resolution, precision, accuracy are always pushing forward. What was deemed ‘adequate’ 10, 15, or 20 years ago may not be considered such today.

Evan has data that defies a ready physical explanation. If he applies the MMTS Bias I adjustment and the unphysical result disappears, he will actually be able to describe a very paper worthy result – regardless of whether the trends move up, down, or sideways.

I think she was putting the curse on me, actually. But it’s too late. It’s sort of a reverse-Dorothy-Parker: I love homogenizing, but I hate having homogenized.

Yes, on the face of it this does seem contradictory – but I’m not sure it is.Hubbard still saw a negative offset, but the response curve wasn’t flat. So the adjustment at higher temperatures is smaller (0.22 @ 26ºC) than the adjustment at lower temperatures (0.42 @ -9ºC). Between these points the response is linear, but outside of them the adjustment moves in the opposite direction.

Possible, I guess. But it’s have to be a very damn steep curve.

On the other hand, if my results are within throwing distance of Menne’s, and Menne’s are correct for the reason you state, then my results will likely be in the ballpark, as well. And, as i say, J-NG may just step in and handle it.

If he applies the MMTS Bias I adjustment and the unphysical result disappears, he will actually be able to describe a very paper worthy result – regardless of whether the trends move up, down, or sideways.

Tmean is running about zero, but for Menne it is only a 0.02C/decade 30-yr. trend effect. So that is close. When I get to Tmax and Tmin, then I will know if there is significant divergence. My money is on Menne, since I am using a similar approach. But we’ll see. I am working on it.

yup, I was just turning the curse back on it’s inappropriate sending @165, as I don’t believe curses are a proper argument for or against anything. Endless insistence doesn’t make things more real, but it’s really out of my stars, the maths part of it. I do not believe almost all of science for the past two centuries has been in a grand conspiracy to hide the facts: quite the contrary. Endless sniping does nothing but delay understanding.

Several days ago Phil Clarke posted the following historical extracts over at Hotwhopper:

———————————-

“I believe we will be able to demonstrate that some of the global warming increase is not from CO2 but from localized changes in the temperature-measurement environment.”

Anthony Watts
June 17 2007 ( after about 2% of data collected)
Interview with Pittsburgh Tribune.

“It gets worse. We observed that changes in the technology of temperature stations over time also has caused them to report a false warming trend. We found major gaps in the data record that were filled in with data from nearby sites, a practice that propagates and compounds errors. We found that adjustments to the data by both NOAA and another government agency, NASA, cause recent temperatures to look even higher.

The conclusion is inescapable: The U.S. temperature record is unreliable.”

Is the U.S. Temperature Record Reliable?
Anthony Watts
Publisher: Heartland Institute.
Peer-reviewed? No.

“Instrumental temperature data for the pre-satellite era (1850-1980) have been so widely, systematically, and uni-directionally tampered with that it cannot be credibly asserted there has been any significant “global warming” in the 20th century.”

“The Earth is warmer than it was 100-150 years ago. But that was never in contention – it is a straw man argument. The magnitude and causes are what skeptics question.”

WUWT.
Anthony Watts
Oct 21, 2011 (Post-BEST)

———————————-

These are the words of a person who has made himself into an object of ridicule and adds to that impression almost daily. What can we say about someone who has become a willing acolyte to such a monumental buffoon? Talk about recursing…

To construct a more appropriate albeit mixed military metaphor, EJ is P.L.O. to Tony’s ex-PFC Wintergreen. That people like this can continue to express triumphalism as they do is a puzzlement. Or is it just that, like conservatism itself, denial can never fail, it can only be failed?

2007 was back in the USHCN1 days. At that time, FILNET caused as great a trend increase as TOBS adjustment.

So what Anthony said was true at the time, and I agreed. But we are way past USHCN1 now. I am currently adapting from USHCN2.0 to 2.5.

At first we thought it was the change in microsite causing a bias in the trend. What we found, using Leroy (2010), was that unchanging bad microsite turns the trick as well, at least for as long as a trend persists (our hypothesis is very fickle — it goes with the consensus).

“The Earth is warmer than it was 100-150 years ago. But that was never in contention – it is a straw man argument. The magnitude and causes are what skeptics question.”

After all the times i have seen this, I still don’t what problem anyone would have with that statement.

Kevin, my suspicion is that these folks have passed up several potentially publishable results due to their pre-announcement that the paper is going to be Very, Very Big. Probably eventually n-g will lose patience and demand that *something* be published.

I have to say it is interesting to see this sort of outside speculation from the inside.

I am telling you, we weren’t ready. if anyone knows that, I do. We got through Fall et al. peer review rather easily. The results showed no microsite effect for Tmean, so no one even mentioned TOBS or moves or MMTS, either.

That won’t be the case for the current paper.

One thing I’ll be doing for my own amusement is running our current stationset using Leroy (1999) as we did in the Fall paper. And if I get similar results to we are getting now I will have one heck of a Scientific Horse-Laugh all saved up.

So the adjustment at higher temperatures is smaller (0.22 @ 26ºC) than the adjustment at lower temperatures (0.42 @ -9ºC). Between these points the response is linear, but outside of them the adjustment moves in the opposite direction.

BTW, this is the sort of useful concatenation that saves me time. Thanks. You need to understand how utterly overwhelmed I am at present with this project. Nearly all stations are within those limits, I think. I have time to pursue this approach I will. Or, as I say, J-NG will handle it.

Also interesting. We suspect that cases of extreme heat sink presence (the worse of the class 5s) behave the same way: It becomes so swamped that although the offset is likely through the roof, the trend itself will be swamped and therefore diminished. We do not know what this limit is, however, or how much effect it might have.

Yes, I am fully away of temperature measurement and siting discussions and the one bit of useful work Anthony Watts and his colleagues did on that some time ago. It seems that the end result of evaluating siting demonstrated that there was no conspiratorial bias. The insistence that anything whatsoever that discredits the vast confluence of information pouring in that confirms that over time the greenhouse effect is a greenhouse effect and is having consequences is obsessive and wrong. The fact that many people would like to ignore this and will seize any straw doesn’t make any bricks from those straws. The planet is not political. Scientists are by nature skeptical, that’s how they work. This insistence is not skeptical at all, as it hews with great intensity to what it wishes to believe, and doesn’t apply any intellectual rigor or curiosity whatsoever to the vast bulk of information it chooses to believe is false and the result of some global centuries long science-wide conspiracy.

One of the most fascinating efforts of the contrarian universe is to detach temperature from temperature and try to indicate that different ways of measuring it make it a different thing altogether. While scientists try to tease out ways of evaluating how proxy and anecdotal records can be confirmed and utilized, others just try to persuade people that temperature is not temperature if it’s measured with a thermometer instead of a satellite (to take the most extreme and obvious example of distortion).

Meanwhile, the inaccessibility of certain regions (Arctic and Antarctic) is also played for all it’s worth as well, pretending it is a failure by scientists rather than real-world obstacles.

As soon as some intelligent way of overcoming these obstacles appears, it seems important to our top misleaders to yell fraud at the top of their lungs. Oddly, there are many who are so eager to find something to complain about that they don’t think it through. It doesn’t seem to occur to them that it is dangerous to multiply dishonest talking points and mislead the world’s population about genuinely dangerous findings.

It seems that the end result of evaluating siting demonstrated that there was no conspiratorial bias.

That was Fall et al. (2011). I was co-author on that (and made the ratings). We found the Tmin effect, but there was no effect on Tmean. The problem was that we were using Leroy (1999) which rated only for distance to sink and not the area of the sink. A few months later I (rather dispiritedly) took the updated Leroy (2010) and started re-rating the stations. That was when I made the discovery.

It operates no matter how we bin or how we divide the data, so it is not a binning artifact. It works to exaggerate trend, be it warming or cooling. With or without TOBS-adjustment, MMTS conversion or moves, for that matter. Even the set of stations we dropped, measured separately, show the same thing.

We have addressed harsh independent review and have addressed all major points.

The fact that many people would like to ignore this and will seize any straw doesn’t make any bricks from those straws.

That is a sword that cuts both ways. But don’t create a false dichotomy. The serious skeptical scientists are nearly all lukewarmers to one degree or another (Lindzen, Spencer, both Pielkes, etc., etc.). The dividing line between the lukewarmers and those urgently calling for action is not the raw forcing effect of CO2, but the issue of positive feedbacks.

Not that positive feedbacks don’t exist. (Our heat sink hypothesis is a prime example of positive feedback.) The question is what the net is (or is not).

The planet is not political.

Hmm. Ever see two herds of deer staking out the same turf? Or a crow announcing his claim to an outcropping?

Besides, this argument is not going to be settled by politicians. It will be determined ultimately in the journals, the lab, and the field. Meanwhile, politicians will pigpile; its what they do. Sound, fury, nothing.

it seems important to our top misleaders to yell fraud at the top of their lungs.

I made my feelings on that matter crystal clear, upthread. I also made it clear about my response to those who will inevitably exploit our paper to claim that it “disproves” AGW.

One of the most fascinating efforts of the contrarian universe is to detach temperature from temperature

And that would be me and the gang entering, stage left. What we did was detach temperature (the well sited stations) from temperature (the poorly sited stations) and compared the trends. Got a problem with that?

and try to indicate that different ways of measuring it make it a different thing altogether.

That too, yeah.

While scientists try to tease out ways of evaluating how proxy and anecdotal records can be confirmed and utilized,

I do that.

others just try to persuade people that temperature is not temperature if it’s measured with a thermometer instead of a satellite (to take the most extreme and obvious example of distortion).

I do that, too. Not literally, but in the way you mean. But I only do part B after having done part A. (Satellites, of course, measure LT, not SAT.)

Oddly, there are many who are so eager to find something to complain about that they don’t think it through.

BTW, the curse you put on me is nothing to The homogenization curse, the horror with which I have been afflicted.

There are two types of people in this world: Those who have homogenized and those who have not homogenized. I am in the former category. And once you are a homogenizer, you are a homogenizer for the rest of your life.

It is fun. It is empowering. It (forcibly) makes you think you know more and more the less and less you actually know. Here be dragons.

Here you have a very strong point: both JoNova and Goddard have become lunatic fringe attention whores who seem to be trying to hijack the entire skeptical movement with maverick claims, but only end up being thus marginalized. And in cultish fashion they indeed have their supporters. I still lament the lack of actual testosterone in the skeptical movement, so all you see are outspoken pacifists and insider grumps. This blog entry is extremely witty and powerful with internally self-consistent impact. I could have written it myself, were I detached enough and less partisan. I hope such wit means you retain a sense of humor, even though you are in an inquisitional Gaian doomsday cult. I myself refer to Goddard as a “raw data maverick” and loudly and often drunkenly condemned in quite nasty fashion Nova’s “wiggle matching PR stunt.” Early on I also savaged Watts in cooperation with Nova for dragging his surface stations project on for years minus actual results, using mere photographs of bad stations to imply that the temperature record was corrupt. None of this compares to the well funded bladeless input data of the latest Marcott 2013 fake hockey stick that a coauthor described to NY Times reporter Revkin as a “super hockey stick” though. A lot of seemingly mainstream skeptics have gone over to the dark side, trying to fight alarmist propaganda with skeptical propaganda. That’s even worse than pacifism. That’s a clown suit.

dragging his surface stations project on for years minus actual results, using mere photographs of bad stations to imply that the temperature record was corrupt.

We go just a wee bit further, I think. And we had the results, writ large, two years ago. But we have dealt with criticisms and while i think it safe to say the record is exaggerated by at least 50%, i can’t say until I have reworked the USHCN 2.5 stats.

I want to add that I sympathize with the problem NOAA is having with this. First, they were criticized for the trend bump as a result of FILNET. So they try to fix the problem via a sophisticated homogenization process — and it all checks.

Couldn’t be microsite because that is just jumps, right?

Except not.

Yes, microsite change is a jump. (Or not.)

But bad, unchanging microsite is is a subtle trend bias that seeps into every pairwise comparison they made.

That is the basic journey our crew has made: Offset-trend to Delta-trend, if you will.

NOAA could fix that by binning for microsite. But they would have to consistently rate every station, including the COOP Class As they use for pairwise. (Do those “proximity views” I do, using GE circles and area polygons.)

Then, at least, homogenization, while still being mush, would be aggregate mush within the aggregate ballpark, for the greater good, I’m sure (which is what “performing as advertised” means.)

But if there is a nasty-but-subtle trend bias, and microsite is just that, the mush slops over the side (which is also what “performing as advertised” means). Furthermore, offset aspect of microsite has the aspect of a red herring because you find that and stop there. (Unless you don’t. I didn;t.)

I remember books, movies, even TV episodes from my arguably misspent youth concerning the identification of outliers. The question as to whether said outliers would be “corrected” or “dropped” made for amusing variation.

Thing is, you start with wanting to remove all of the stations that don’t sing the same song. (And the less metadata, the better.) Whereas I want to find well sited stations with good metadata, no moves, and no TOBS shifts — and then let them sing whatever song they want.

After my version of Big Brother, there are a lot fewer stations left standing. But those that are, are good. You have ten times as many, but four out of five no good and homogenization causes them to corrupt the others:

Your trend result is higher than if you just took an average. And the trend average is way to high, to begin with, thanks to microsite.

Let me ignore all the other statements I do not agree with. You make one central statement I would love to understand the basis for.

Evan Jones: “”But bad, unchanging microsite is is a subtle trend bias that seeps into every pairwise comparison they made.”

I have asked several times for evidence for this statement and have the feeling I have never gotten one. What is your evidence for the claim that the problem is a gradual change in the temperature at the bad stations?

Just finding a trend bias is no evidence what so ever. You can get a different trend due to a jump or due to a gradual change.

It seems pretty clear to me: When pairwise comparisons are made, microsite is not currently taken into account, stations with good microsite are paired with those that are bad.

Therefore, the bias is transmitted.

If one wants to avoid this, one could either drop all the bad stations and homogenize only the good ones, or pairwise compare (both bad and good) with good stations only, using the good station average as a basis to establish outliers.

The former would result in a homogenized set of Class 1\2. the latter would be a homogenized full set, but one adjusted to conform with 1\2 stations.

As it is, 4 out of 5 pairwise comparisons are with stations with poor microsite. This not only occurs during overall homogenization, but also with MMTS-adjustment, which is also based on pairwise comparison. (I can easily see why Menne is playing with combining the two processes.)

By the way, Victor, I am coming up with interesting results in my regional-as-pairwise attempts to correct for MMTS. I am going 5 years per side (which is plenty, for a jump), which effectively homogenizes a third of our study period for three quarter of our stations. You fiend.

I am not going to overdo it. One recursive only. I have made the first pairwise adjustments. USHCN has near-perfect metadata for MMTS conversion (and always did), so I need not (and must not) go hunting hobgoblins. Next time, maybe.

I will now do the second (and final) pairwise between raw and 1st-pairwise-adjusted and that will be all the smoothing I can handle. (I wouldn’t even go that far, but I want to make some accounting for overlapping conversion times. Less of a problem for the Class3\4\5s because there are so many.)

That will produce the final adjusted data. And lord have pity on my miserable homogenizing soul.

As an aside, it will also be a crude but interesting look at what the differences, if any, there are between regions. (Uniformly pouring on MMTS adjustments out of Hubbard or Menne won’t really do this.)

I am, of course, splitting the stations into compliant (Class1\2) and non-compliant groups (Class 3\4\5) and only homogenizing each with its own kind, never with the other. It will be interesting to see if MMTS effect on compliant is the same as on non-compliant stations.

Stipulating that microsite is as I say, the question is answered each and every time the plaintiff pairwise-compares a Class 1\2 site with a Class 3\4\5 site.

Try this test: Homogenize the Class 1\2s separately, pairwised only with other Class 1\2s. If the result is the same as for Class 2s homogenized the way NOAA does it, you will have proven your point. If the result is significantly different, then I have proven mine.

I haven’t been plugging stuff into homogenization software. I am doing this myself, by hand, piece by piece, step by step. I can see what the numbers are doing every step of the way — and why.

Besides, why would you object to binning GHCN by microsite. If I’m right, then it is completely necessary. And if I’m wrong, then no harm done. All you’d have to do is find someone to rate them . . . #B^)

Seriously, my dear V^2, surely you can see that — IF — microsite does transmit a trend bias, that this will seep into all pairwise comparisons between well sited and pootly sited stations.

If there is no bias, of course, this will have no effect. I am not talking “S”– but if Delta-S exists — if there is a delta — then surely you must understand that this will introduce a bias into such cross-comparisons.

Do we at least agree this far — with me well understanding that you do not necessarily accept that microsite is a bias in the first place?

Okay, the new study on tornadoes sure looks as if it could have profited from pre-pub independent review.

Also, a revision to the above: it looks as if I am going to have to do the MMTS for all stations of all classes: There aren’t enough Class 1\2 stations per region to adequately cross-compare with each other (when I did so, the average was +.193/decade adjusted vs. + +.192 raw).

This will be criticized, so I will cave and do both well and poorly sited stations together and let the chips fall as they may.

It’s not pretty when the anti-science denial thugs try to setup fake-front “legit” orgs to launder their ideas.

How many have they burned through now? From SEPP and The Tobacco Institute to Heartland, the NIPCC and now Anthony’s little coin collector OAS. You have to be pretty gullible to see this as anything but exploitation of the mentally deficient.

PT Barnum said there’s a sucker born every minute. What he didn’t know is that they’d tend to gather together under the umbrella of science denialism.

Evan writes: “Okay, the new study on tornadoes sure looks as if it could have profited from pre-pub independent review.”

Perhaps you ought to read it. Or make an actual factual criticism. AW and Joe D’Aleo apparently didn’t read it before they issued an official OAS statement!? Else they just failed to understand it.

Joe D’Aleo has been caught peddlling crap many times before, but this time he and AW manage to use 4 different graphs all of which are irrelevant to the subject of the paper being discussed. In fact, reading their statement you can’t actually tell what the paper had to say. They never mention its main conclusion. LOL

Oh, and they also wrote: “It is the opinion of The OAS that this sort of methodology to remove a portion of a dataset to cite a result is unsupportable and without justification.” Please bear that in mind as you slice and dice the temperature data sets. LOL. This level of stupidity and hypocrisy ought to win an award.

“We demonstrate that peak tornado activity has shifted 7 days earlier in the year over the past six decades in the central and southern US Great Plains, the area with the highest global incidence of tornado activity. “

A fairly commonsense result given the dozens of phenological studies that show that spring is arriving earlier each decade. Obviously the tornado season is tied to the circulation patterns that develop in late spring/early summer and if they’re occurring earlier, then tornados should as well. D’Aleo and Watts apparently misinterpreted this as saying more tornados are occurring … reading is hard …. for deniers.

Improved detection increases the number of tornadoes in a given year due to increasing the probability of detection. There is no reason to believe that tornadoes early (late) in a given year are more (less) likely to be detected.

Evan – remember that they’re looking at the *peak* activity. The center of the distribution. We might expect that since more tornadoes are detected the very earliest and the very latest dates would change, but why would the center of the distribution shift? There is no logical reason. Simply stating more tornadoes are detected is irrelevant to where the center of the distribution resides.

Evan: “Okay, the new study on tornadoes sure looks as if it could have profited from pre-pub independent review.”

Kevin: Perhaps you ought to read it. Or make an actual factual criticism.

Evan still hasn’t learned that Anthony Watts is totally untrustworthy.

Anthony says there’s some kind of problem with Long & Stoy 2014. But the problem is with Anthony’s lack of understanding, not with the paper itself. Nonetheless, Evan accepts Anthony’s judgement and, without checking it for himself, repeats it over here.

Anthony Watts is a fool. Trusting Anthony Watts as a source makes you look like a fool too.

Actually, it’s rather depressing to read the comments in the “OAS” thread about this paper at WUWT. There are literally dozens of comments that just mindlessly cheer on Anthony’s nonsense. Then Harold Brooks (an actual scientist) appears, and explains to Anthony exactly why he’s wrong. A few people seem to get it, but mostly Brooks’s comment is just wasted on them. Anthony, true to form, refuses to admit his own error and engages in a lot of handwaving instead.

Evan Jones: “The evidence is, simply, that I have made the year-to-year graphs and the divergence appears gradual, not jumps.”

Do I recall it correctly that your claim is that during a period of 30 years, the well-sited stations have a trend of +0.185C/decade and the badly-sited stations have a trend of +0.335C/decade? The difference over the full 30-year period is thus (0.335-0.185)*3= 0.45°C.

I do not think you can eye ball whether a time series has two inhomogeneities (a 30 year period will on average contain 2 inhomogeneities) of 0.2°C or a gradual trend of 0.45°C over 30 years. You would have to eye ball that in a station temperature time series with a standard deviation of about 0.7°C and a trend of about 1 degree Celsius. I think you should probably use nearby reference stations to reduce the noise and see the non-climatic changes more clearly. And you should use a statistical test to see if the statistical trend model is clearly superior over a statistical step model.

To which I responded (comment removed by BS), “Brandon – Please explain how the width of a tree ring in a data set labelled ‘precip’ varies from the width of a tree ring labelled ‘temp’ – or is the measured width independent of which parameter you’re intending to analyze?

Alternatively, I suppose there could be really tiny writing on the tree rings that say, “Only valid as precipitation proxy.”

More knowledgeable people may correct me, but as far as I know it does matter where the tree rings come from. If you want to use tree rings to measure temperature, you have to take trees from regions where growth is mainly limited by temperature (near the tree line).

Victor, I think you’re missing the point. The tree ring widths themselves tell us nothing directly about temperature, precipitation, or any other environmental factor. They’re just a normalized record of various growth width ratios.

The analysis tells us whether the record is correlated to temperature, precipitation, PDSI, or any other factor under study. It’s possible for one data set to be highly correlated to more than one of these factors. It’s possible that the ratios aren’t correlated to *any* of the factors of interest. Again, it’s the analysis of the proxy record that determines its fitness – not how it’s used initially or how we briefly abbreviate and categorize it in a list.

So the notion that Brandon latched onto – that MBH98’s proxy data was flawed because it used proxies marked in a list as ‘precip’ – is just ignorance.

Come, now, Victor, you know that I have always given NOAA fair credit for their metadata, and for the fact that they oversample.

Not seeing a specific definition of ‘clean metadata’ I don’t want to jump the gun, but oftentimes ‘clean’ records are the ones where problems haven’t been identified *YET* :)

Poorly defined on my part. What I mean is stations that have not moved and are not subject to TOBS bias.

So, is it possible what you’ve done is identified the 400 stations with the worst metadata records?

An amusing concept.

But even the stations with poor metadata support our hypothesis. The stations we dropped for poor metadata have even lower trends than the ones we kept, and the gap between well and poorly sited dropped stations is the same as for the ones we did not drop.

But you’re basing this assumption that they haven’t moved solely on the metadata? How do you know the metadata isn’t wrong?

All I can say is that — for USHCN back to 1979 — it appears to be excellent. Menne (2010) gave the MoEs on the metadata from 1980-2009, and there has been dramatic improvement over the last 5 years — for USHCN. It was always spot on for MMTS conversions, at any rate.

Besides (I repeat), the stations we dropped, on average, have lower trend than the “unperturbed” stations we kept.

I don’t know what the state of the non-USHCN COOP stations. We may be looking into that pretty soon. (I am not necessarily optimistic.)

There is also evidence in the fact that the data shows no real step jumps: the divergence between compatible and non-compatible siting (and the “official” adjusted stats) is as smooth as silk, as far as these things go. (Dr. Venema will be most pleased, I’m sure.)

What’s your criteria for determining the quality of the metadata?

Oh, I didn’t mean it that way. I meant a station with “bad” metadata is one that shows moves or TOBS-bias. By now, all the USHCN stations have good metadata for the recent period we are studying.

As an aside, I find my own MMTS adjustments are coming out too small. Plus, they are only regionally homogenized. So I will swallow the bitter pill and apply the Menne (2010) jumps (+.0375 to Tmean, +0.1 to Tmax, and -0.25 from Tmin) after point of conversion. We may get into it deeper, but that would require another paper.